Most local AI setup guides assume things just work. They don't. ROCm refuses to detect your GPU, Ollama throws cryptic CUDA errors, and the GitHub issue thread is 200 comments deep with no resolution.
Georgia writes the guides that end the debugging spiral, step-by-step, tested on the hardware CraftRigs readers actually own. She tests on the same used RTX 3090s and budget builds her readers run, because a guide that only works on a $6,000 workstation isn't a guide.
Your Threadripper already owns the 70B path—CPU-only hits 2.1–7.1 tok/s on DDR5 bandwidth, beats dual RTX 3090 cost for batch jobs, and leaves GPU free. Honest benchmarks, no GPU required.
May 22, 2026
Wrong driver kills AMD ROCm on Windows—ROCDXG not Adrenalin, clean install required. Full consumer GPU matrix with Linux native, WSL2 grades, and honest tok/s numbers. Install right, skip the forum archaeology.
May 22, 2026
Mac Studio 128GB killed in May 2026, M5 Ultra pushed to Q4—M4 Max 96GB is your only 70B Q8_0 option until 2027. We map every tier, benchmark AMD's Strix Halo rival, and tell you whether to buy or wait.
May 22, 2026
8GB Blackwell or 24GB used Ampere? RTX 5060 Ti 16GB hits 65 tok/s on 13B, but only RTX 3090 runs 70B models. Match GPU to your model size or waste money.
May 22, 2026
NVIDIA cut RTX 50-series production 40%—street prices run 18–67% over MSRP. RTX 5060 Ti 16GB at $485 beats the stack, but used RTX 3090 at $500 undercuts everything. Match card to budget before Computex hype resets the board.
May 22, 2026
Your $250 Arc B580 runs Mistral 7B at 13 tok/s via Vulkan—leaving XMX acceleration idle. IPEX-LLM Docker hits 70 tok/s with full setup steps for Linux and Windows. Stop running at 20% speed.
May 22, 2026
LM Studio shows 0 GPUs detected? NVIDIA CUDA 12.2, AMD ROCDXG May 2026 driver, and WSL2 passthrough each have distinct fixes—most are driver mismatches, not hardware failure. Run the 60-second pre-flight, then follow your platform path.
May 22, 2026
Native Ollama locks you to one machine—Pipelines unlocks llama.cpp, remote Ollama, and custom APIs in the same chat UI. New April 2026 Desktop App auto-recovers GPU crashes. Wire your backend once, switch models freely.
May 22, 2026
HBM and GDDR7 shortages keep RTX 50-series prices 40% above MSRP. Used RTX 3090 24GB at $500 beats new cards on $/GB-VRAM. Here's how to navigate the chaos and buy smart.
May 22, 2026
12GB is the 14B-model sweet spot. Arc B580, RTX 3060, and RTX 4060 Ti compared on tok/s, price, and which OS plays nicely.
May 9, 2026
CUDA OOM has different root causes on Windows, WSL2, and bare Linux. Diagnose first, then jump to the fix that matches your platform.
May 9, 2026
Three real build tiers for local LLMs in 2026. What 12GB, 24GB, and 48GB+ VRAM actually run, and where each tier hits its wall.
May 9, 2026
Three tiers of local inference — desktop GPU, Apple Silicon, mobile NPU. Where each wins, what they actually run, and how to pick a stack.
May 9, 2026
Apple Silicon laptop or desktop GPU rig for local LLMs? Decision framework by model size, battery, thermals, and price tier.
May 9, 2026
EVO-X2: 256 GB/s unified memory (273 GB/s observed). Delivers 14–18 tok/s on Qwen 3.6 35B Q4_K_M. Compare Mac Mini M4: 120 GB/s. See specs, thermals, BIOS quirks, $1,500–$2,200 pricing, and when to buy.
May 8, 2026
Does fanless Air M4 sustain LLM inference? 30-min test: Air peaks for 8–12 min, drops 25–40%. Pro M4 stays flat. Air for chat, Pro for agentic.
May 8, 2026
Route Ollama, vLLM, llama-server, and cloud APIs from one Open WebUI interface using Pipelines. Docker Compose setup in minutes; automatically switch backends by model or context length.
May 8, 2026
32B models don't fit 16 GB VRAM without offload—expect 5–10 tok/s. Better: 27B Q4 at 30–40 tok/s (no offload) or MoE 35B-A3B at 12–18 tok/s. Use the VRAM table to find your fit.
May 8, 2026
Qwen 3.5 35B Hermes on RTX 3090: fits at Q4_K_M with 32k context. Tok/s benchmarks, Hermes vs base, and best quantization choice.
May 8, 2026
12GB GDDR7 runs Qwen 3.6 7B at 140 tok/s but 27B spills to CPU (6 tok/s). RTX 5060 Ti 16GB runs 27B at 25 tok/s. For LLMs, choose VRAM over Blackwell specs.
May 8, 2026
Phi-4 14B Q4_K_M won't fit your 8GB card at Q4 with usable context—but 12GB handles 8K, 16GB+ handles 32K. Exact VRAM per quant tier, GPU fit table, and decode benchmarks.
May 7, 2026
Gave up on local coding? Qwen 3.6 27B Q5 hits 94% accuracy, Reddit consensus May 2026. DeepSeek V4: cost-collapse. Phi-4 14B: 12GB fit. Match model to hardware, not guesses.
May 6, 2026
Burned-out mining cards and relabeled 3080s cost buyers $300–$450—eBay resolves only ~75% of GPU disputes in 10–30 days. Demand GPU-Z sensors, timestamped cuda-memtest video, and 30-min llama.cpp burn-in before you buy.
May 6, 2026
16 GB GPU chokes on Qwen3.6 MoE—OOM or 2 tok/s without flags. --n-cpu-moe 20 --split-mode row hits 18–28 tok/s at 11.2 GB VRAM, but --fit on alone degrades 40–60%. Pin experts first, or don't run it.
May 6, 2026
Stop crashing OOM. Map 70B layers to 16GB VRAM with precise -ngl values, see 3.2 tok/s vs 8.5 tok/s degradation, and know when CPU offload beats dropping to Q3_K_S. Budget Builder guide.
May 5, 2026
Stuck with 8GB VRAM? Qwen 3.5 9B at Q4_K_M runs 12.4 tok/s on RTX 4060, 8.7 tok/s on RTX 3060—verified benchmarks, exact quants, and copy-paste configs for code, chat, and agents. Stop waiting for a GPU upgrade and start running local LLMs today.
May 5, 2026
24GB RTX 3090 OOM at 4K context? KV cache eats 10.5GB before weights load. Our 3-step fix order recovers 8-14GB: KV quant → Flash Attn → last-resort weight cut. Stop guessing, start measuring.
May 5, 2026
AMD GPU OOM errors hide 3.2GB of dark matter VRAM that CUDA doesn't. Learn the 4 symptom signatures, 8 diagnostic commands, and ROCm-specific fixes that NVIDIA guides never include.
May 5, 2026
WSL2 silently caps GPU memory at 50% and breaks mmap() passthrough — here's the exact .wslconfig, driver 550+ requirement, and build flag that fixes CUDA OOM for 70B models at 7.8 tok/s.
May 5, 2026
Stop buying VRAM you don't need. 73% of CUDA OOM errors fix in 45 seconds with browser kills and KV cache trims. See the ranked recovery tree with proof from 847 real crashes.
May 5, 2026
DeepSeek V4's 1T MoE needs 48GB VRAM minimum—0.8 tok/s on RTX 4090, 3.2 tok/s dual 3090 at 32K. 1M context? API wins at $0.80/M tokens. See exact builds.
May 5, 2026
Wasted 3 days on wrong Gemma 4 size? Map 4B/12B/26B MoE/31B dense to your exact VRAM tier with quant picks—8GB to 48GB, copy-paste configs, 14.2 tok/s benchmarks. See why MoE beats dense on 24GB.
May 5, 2026
GLM-5.1's 744B MoE runs at 13.2 tok/s on dual RTX 3090—38GB real floor, not the 400GB myth. Build $2,040 rig, beat cloud API costs at 340 tasks/month. See quant tiers & throughput proof.
May 5, 2026
Intel Arc Pro B70 gives 32GB VRAM new for $949—here's the exact Windows driver, OneAPI, and llama.cpp OpenVINO build that hits 4.2 tok/s on 70B models.
May 5, 2026
Need 192GB VRAM for 671B models? This $9,847 Intel Arc Pro B70 workstation loads what NVIDIA can't—4.2 tok/s 70B, 2.1 tok/s 122B, 0.31 tok/s 671B. Full parts list and benchmarks inside.
May 5, 2026
Stop crashing Llama-3 70B on your RTX 3090. Our tested -ngl values, KV cache q4_0 flags, and flash attention configs hit 6.5–8.2 tok/s at 4K context—no second GPU, no cloud bill. Get the exact command lines now.
May 5, 2026
Stuck at 1 tok/s with llama.cpp CPU offload? Calculate exact layer splits for 70B models—get 4.2 tok/s on 24GB VRAM with precise -ngl flags, quant picks, and bandwidth math. (Benchmarked: 3090, 4090, DDR5 configs)
May 5, 2026
Stop guessing why your 70B model won't load. The real Q4_K_M memory formula: 0.58 bytes/parameter + KV cache that grows with context. Worked 7B to 70B examples inside.
May 5, 2026
Stop crashing at load. 16GB VRAM quant cheat sheet: which Q4_K_M, Q5_K_M, Q8_0 fits 7B/13B/20B/30B models with real context lengths. Budget Builder tested.
May 5, 2026
LM Studio shows 0 GPUs? 73% of 'fixes' skip the real problem. Verify driver-runtime coupling, fix WSL2 passthrough, match CUDA versions—then load models at 8+ tok/s. Start the diagnostic tree now.
May 5, 2026
Run Qwen3.6-27B at 12+ tok/s on 24GB GPUs with exact Ollama Modelfile flags, llama.cpp commands, and quantization math for 32K-64K context.
May 5, 2026
Your 24GB GPU chokes on 31B dense models. Gemma 4's 26B MoE loads 4B active params, hits 28 tok/s with 32K context—here's the exact Ollama setup that unlocks it.
May 5, 2026
WSL2 CUDA broken for local LLMs? Fix GPU passthrough, driver conflicts, and the hidden mmap() trap that cuts token speed 60%. Build llama.cpp right on Windows—full guide.
May 5, 2026
AMD ROCm 6.4 supports RDNA 3–5 and Strix Halo. RX 7900 XTX reaches 85% RTX 4090 parity on Hipfire. Ubuntu 24.04 install recipe included; Vulkan fallback for older AMD GPUs.
Apr 30, 2026
24GB local rigs max out at 18 tok/sec; 48GB doubles throughput. Most mid-tier users find hybrid (local 24GB + Opus fallback) cheaper than upgrading.
Apr 27, 2026
Q1 2026 models demand 48GB. Learn why Mistral, Nemotron, and Gemma converged on this floor, which GPUs hit it, and single vs. dual-GPU cost breakdown.
Apr 27, 2026
April 2026's TurboQuant and KV-quant wave broke the old 8GB limits. Run 7B at 8K context, not 3B at 2K. Discover realistic ceilings, quantization strategies, and Ollama configs that work today.
Apr 27, 2026
Discover the $47K/quarter API migration math—and calculate yours. Tier 1–3 hardware payback models, real ROI scenarios, breakeven calculator. 18–24 month ROI on $2K+/month spend.
Apr 27, 2026
RTX 4070 wins at $500 for 7B–14B local LLM inference—12GB VRAM, 16+ tokens/sec on 14B, full NVIDIA support. Compare RTX 4070, used 3090, and 4060 Ti. See throughput benchmarks and when to buy used instead.
Apr 27, 2026
70B Q4_K_M CUDA OOM on 24GB? Likely a page fault. Diagnose and fix with diagnostic commands, KV cache math, and tuning steps. Avoid costly hardware upgrades if it's just paging.
Apr 27, 2026
Deploy Gemma 4 free on Oracle ARM—24GB, no credit card. Step-by-step setup, real throughput benchmarks, and when free tier works. Learn the limits. Deploy now.
Apr 27, 2026
Your 16GB GPU seems too small for 70B models. Learn the -ngl layer math that makes it work, with formulas, benchmarks, and production readiness checks.
Apr 27, 2026
RTX 50-series CPU-only in LM Studio? The 4 proven driver-CUDA combos. 40–50x faster inference. Why it fails and how to fix Blackwell GPU detection.
Apr 27, 2026
Run MiniMax M2.5 locally on multi-GPU rigs. Dual RTX 3090 = 8 tok/sec; dual 4090 = 20 tok/sec. Complete vRAM math, hardware tiers, and tensor parallelism setup.
Apr 27, 2026
Qwen3-Next Thinking hits 1M context on M4 Ultra MLX at 1.4–2.1 tok/s. See benchmarks, memory math, context tiers, and when Mac reasoning makes sense for your workflow.
Apr 27, 2026
ROCm 7.2 + RX 9070 XT Linux setup: gfx1201 targeting, kernel pins, env var order, and fixes for HSA crashes and inference hangs. Step-by-step guide for RDNA4 local LLM inference.
Apr 27, 2026
Hit PCIe bottlenecks on 3x 3090? Threadripper TRX50 + 4-GPU scaling unlocks 8x bandwidth. Full upgrade path, cost breakdown, and 70B/405B benchmarks inside.
Apr 27, 2026
Mac Studio orders halted April 2026. Wait for M5? Switch to RTX 3090 ($3600)? Buy leftover M4 Ultra stock now? Find your answer with our decision guide.
Apr 26, 2026
Intel's Arc Pro B70 hit Newegg #1 this week. Is it right for local LLMs? Compare real inference benchmarks, 32GB options under $1k, and find the best GPU for your budget.
Apr 26, 2026
Arc Pro B70 SYCL llama.cpp: achieve 22.5 tok/s on Qwen3.5-27B. Full tuning guide with reproducible benchmarks, flag explanations, and configuration walkthrough included.
Apr 26, 2026
CUDA out-of-memory in llama.cpp on WSL2 is usually tuning, not hardware. This guide diagnoses your failure mode—startup crash, first-token cache explosion, or mid-batch buffer thrash—and delivers 15 reversible fixes ranked by simplicity, from num_ctx tuning to layer offload math, plus the Windows reserved-VRAM tax nobody mentions.
Apr 26, 2026
CUDA OOM in Ollama? One of five config issues (num_ctx, parallel slots, mmap, cache, KV quantization). Learn the exact fix for each—all take 5 minutes and work on 8–16 GB GPUs.
Apr 26, 2026
Gemma 4 31B dense vs 26B MoE VRAM requirements, inference speed, and quality comparison. Find the right variant for 8–16 GB budgets with a decision tree.
Apr 26, 2026
Gemma 4 VRAM breakdown: 9B fits 24 GB GPUs (Q5/Q6), 26B MoE needs 48 GB, 31B dense needs 96 GB. Q4/Q5/Q6 tables, MoE active-parameter math, 262K context overhead. Find your GPU tier now.
Apr 26, 2026
GPT-5.5 drops to $0.02/1K tokens; DeepSeek V4 cuts 50%. New break-even thresholds reveal when RTX 3090 beats cloud APIs. Should you buy hardware now?
Apr 26, 2026
Run Llama 70B on 32GB RAM using CPU offload in llama.cpp. Real speeds: 2–5 tok/s CPU-only, 18+ tok/s hybrid. Quantization floor, decision tree.
Apr 26, 2026
8GB VRAM limits context length. Discover how KV quantization + layer offloading unlock 32K safely. Concrete tuning configs for 3060 Ti / 4060 / Arc A770 with real tok/s performance.
Apr 26, 2026
GPU driver updates kill LM Studio detection. Here's the proven 3-step reset: clear cache, rebind CUDA, verify. Restores GPU in 5 minutes on Windows RTX 3060+.
Apr 26, 2026
LM Studio stuck on CPU? Your NVIDIA needs CUDA, AMD needs ROCm, Intel needs Vulkan. Find your vendor, install the backend, and enable GPU acceleration for 10-50x faster inference. Step-by-step fix inside.
Apr 26, 2026
Aider + Qwen 3.6 on RTX 5080: <3-sec responses, zero costs, full privacy. Q4_K_M quantization, config, multi-file editing, real benchmarks—build your local pair programmer today.
Apr 26, 2026
Build a retrieval-augmented generation system with Ollama, LanceDB, and Open WebUI. No cloud APIs. Complete wiring guide and troubleshooting for self-hosted RAG.
Apr 26, 2026
Mac's 192GB unified beats RTX 5090's 32GB on context length and power. RTX dominates speed parity and model variety. See real tok/s benchmarks, 3-year TCO, when each system wins.
Apr 26, 2026
Stop paying $20/month for ChatGPT Plus. Run Mistral or Llama locally with Open WebUI + Ollama—30-minute setup, zero fees, full privacy, keep all chat history. Learn the honest tradeoffs and migration path today.
Apr 26, 2026
Pick the right GGUF quantization for your VRAM. Q4_K_M for 8GB, Q5_K_M for 12–16GB, Q6_K for reasoning. File sizes, speed gains, quality drops, and a quick-pick decision table included.
Apr 26, 2026
Qwen 3.6 Plus: 1M context, KV cache 24GB Q4, RTX 4090 800K, 48GB 1M. Terminal-Bench leader. Real latency, quantization guide, hardware floor, API cost breakeven.
Apr 26, 2026
Local RAG on RTX 3060: Mistral 7B Q4 + all-MiniLM + Qdrant stack. No cloud APIs. Real VRAM breakdown, tradeoffs, and 8-second latency on 12GB hardware.
Apr 26, 2026
Skymizer HTX301 promises 700B inference on one card. Decode the marketing: verify benchmarks, calculate true TCO vs. GPU clusters, weigh thermal overhead, and run a pilot checklist. Enterprise 2026 guide.
Apr 26, 2026
Unlock 32K–64K context on RX 7900 XTX with 2-bit KV quantization. 14.2 tok/s, zero quality loss on Llama 3 70B. TURBO2_0 vs TURBO3_0 explained—complete HIP build inside.
Apr 26, 2026
vLLM online quantization swaps precision without redeploying. Throughput +15–40%, accuracy loss 5–12%. When dynamic quant beats static GGUF for self-hosters.
Apr 26, 2026
ROCm llama.cpp chokes your RX 7900 XTX at 20 tok/s — Hipfire hits 50–60 tok/s with native HIP kernels. RDNA 3 only, Linux-only, zero docs. Here's the first working install path, exact build commands, and honest limits.
Apr 25, 2026
Stop buying 8GB GPUs that OOM at 13B. This $1,187 used 3090 build runs 70B models at 8 tok/s — but only if you skip Windows and buy 64GB RAM, not 32GB.
Apr 23, 2026
70B models fail on 16 GB cards. This $1,497 build runs Llama 3.3 70B at 8.2 tok/s with real April 2026 prices — but you'll buy used.
Apr 23, 2026
4090 chokes on 70B models? Dual 3090 NVLink hits 28 tok/s in VRAM—if you dodge $400 bridge scams and unlock hidden LM Studio multi-GPU controls.
Apr 23, 2026
48 GB doesn't run 70B models. One question picks your card: 4090 for 70B+, dual 3090s for 35–42 tok/s—if you survive PCIe.
Apr 23, 2026
Wrong quant kills code accuracy—Q6_K stays near full-precision HumanEval, Q4_K_M works for chat, but agents need Q6_K+ or hallucinate. Match quant to task, not VRAM.
Apr 23, 2026
Dual RTX 3090 gives 48 GB but loses 15% to NVLink. RTX 5090 hits 31 tok/s on 70B yet chokes at 12K. 6-factor matrix inside—if you can afford the wait.
Apr 23, 2026
Qwen3-235B-A22B needs ~140 GB at Q4_K_M — it doesn't fit 24 GB VRAM + 96 GB DDR5. Here's the honest math, and why 30B-A3B is the MoE build that works.
Apr 23, 2026
Your '128K context' model hits OOM at 16K. VRAM growth and tok/s decay from 7B to 70B models—here's which configs actually work on 24 GB GPUs.
Apr 18, 2026
24 GB VRAM walls force CPU offload at 2 tok/s — but 8-channel DDR5 Threadripper hits 7 tok/s on 70B for $3,800. The bandwidth math NVIDIA won't show you.
Apr 18, 2026
Dual 3090 hits 15 tok/s not 25 — here's why. NVLink vs PCIe, 94% speed at $1,400 less, but only with specific motherboard topology.
Apr 18, 2026
Your 4090 draws 447 W for 46 tok/s—Arc B580 hits 22 tok/s at 190 W. Real wall-power data shows which GPU wins on efficiency, not marketing.
Apr 18, 2026
llama-bench skews 15% without a warm-up pass. This exact 10-repetition command sequence produces defensible, shareable results.
Apr 18, 2026
Your 24 GB card can't run DeepSeek V3 at full speed—here's 4.7 tok/s vs Qwen3's 18.3 tok/s reality, with exact VRAM math that explains why.
Apr 18, 2026
Ollama costs 23% speed vs llama.cpp on RTX 4090. vLLM wins 4.2x multi-user throughput but fails 70B. Single-GPU framework verdict with real tok/s numbers.
Apr 18, 2026
Q4_K_M costs 2.3% quality at 70B. Q8_0 needs 40 GB VRAM. Q5_K_M hits 0.8% loss at 16 GB — but only if you set n_gpu_layers right.
Apr 18, 2026
RTX 5090 promises 77% more bandwidth but delivers only 37% faster 70B inference. See exact Q4_K_M tok/s and why the $1,200 used 4090 still wins at 32B.
Apr 18, 2026
Your GPU claims 85 tok/s but hits 12 on real models. 340+ verified llama-bench results: exact Q4_K_M speeds from RTX 5090 to RX 7900 XTX.
Apr 18, 2026
Your Ollama server slows at 4 users while vLLM hits 3.2x throughput at 16. See the crossover for 24 GB and 16 GB cards—and when Ollama still wins.
Apr 18, 2026
x8/x8 PCIe steals 15% tok/s—tested vs 3090, 6 boards that deliver full bandwidth.
Apr 18, 2026
OpenAI costs $18/1M tokens. Gemma 4 + AgentKit 2.0 runs local for $2—if you fix the 34% tool-call failure rate. Hardware tiers inside.
Apr 18, 2026
744B model crashes at 97% load or crawls 0.4 tok/s on CPU. This guide delivers 6.2 tok/s on one 24 GB GPU—if you have 256 GB RAM and disable swap.
Apr 18, 2026
GPT-OSS-20B needs 14.8 GB VRAM at Q4_K_M — not 8 GB. Get exact quants, tok/s on RTX 4090 vs 5060 Ti, and ROCm fixes to stop silent CPU fallback.
Apr 18, 2026
70B Q4_K_M needs 43.8 GB weights + 6 GB KV at 8K context — 24 GB cards OOM silently. Use this formula to calculate before you download.
Apr 18, 2026
Your "AI PC" crawls at 4 tok/s while the NPU sits idle. Unlock 21 tok/s on Intel NPU—only with these specific build flags most guides miss.
Apr 18, 2026
Stop Ollama from duplicating models across apps—run one llama.cpp server, connect 4 clients, cut VRAM 60%. b8825 setup for 24 GB GPUs.
Apr 18, 2026
Your llama.cpp server re-tokenizes every prompt—400ms wasted. Enable prefix cache, cut latency to 12ms, but only if you fix the silent slot ID bug.
Apr 18, 2026
CLIP pipelines OOM at 847 docs. Unified embeddings fit 16 GB, 47 img/s. Open WebUI dimension mismatch kills ingest—here's the fix.
Apr 18, 2026
Ollama wastes 56% of your RX GPU's speed. The 20-minute fix unlocks Wave32 FlashAttention—if you can build from source.
Apr 18, 2026
Seeing 18 tok/s when benchmarks promise 32? Flash Attention in Ollama 0.5.7+ delivers 2.3x speedup, but only on Ampere/RDNA 3 with 3 env vars set.
Apr 18, 2026
Ollama says 'runner started' but crawls at 4 tok/s on CPU? Force GPU layers with num_gpu, verify via nvidia-smi — fixes 90% of silent fails.
Apr 18, 2026
Q5_K used to CPU-fallback and die—now it's 15-25% slower but 0.8 perplexity better. See which 16 GB and 24 GB GPUs can run it without the old OOM wall.
Apr 18, 2026
"3B active" marketing hides 22.9 GB VRAM demand. Run Qwen 3.6 35B-A3B at 38 tok/s on 24 GB cards. MTP unlocks 262K context on 4090.
Apr 18, 2026
Ollama detects 0 VRAM on RX 9060 XT? 38 tok/s GPU fix: HSA_OVERRIDE_GFX_VERSION=11.0.0 on Linux, OLLAMA_VULKAN=1 on Windows—ROCm limited to Linux.
Apr 18, 2026
RTX 5060 trips PSU shutdowns in 10L cases. RX 9060 XT LP runs 18 tok/s at 128W — but only the 16GB SKU. Here's which models actually fit.
Apr 18, 2026
REST chokes at 10+ concurrent requests with 800ms spikes. gRPC hits 340 tok/s vs 210 tok/s—same hardware, broken OpenAI SDK. When each wins.
Apr 18, 2026
Your 24 GB GPU shows 20 GB in WSL2. Here's the .wslconfig fix that recovers 3.6 GB of hidden VRAM—works on RTX 4090/5090, breaks WSLg GUI.
Apr 18, 2026
ROCm doesn't run on Windows—your RX 7900 XTX sits idle at 4 tok/s. Force Vulkan backend: 18 tok/s on 70B models. Requires typo env var, Adrenalin 24.12.1+.
Apr 18, 2026
scription: ""CUDA driver version is insufficient" crashes LLM setup — here''s the 550.90.07 driver fix, with DDU rollback if 570.xx broke everything
Apr 18, 2026
-ngl 99 still shows 0 layers? Your binary was compiled without CUDA/ROCm. Rebuild with explicit flags, verify with ldd, get 30x speedup in 10 minutes.
Apr 18, 2026
Dual EPYC crawling? NUMA warning cuts 6.8→14.2 tok/s. Single-node binding fixes it—if your model fits 128 GB. The exact numactl command inside.
Apr 18, 2026
Your 4090 crawls at 2 tok/s? VRAM spill kills speed—fix with one quantization tweak, verify in 60s. Works for local LLMs only.
Apr 18, 2026
Ollama crashes Windows NVIDIA? 40% fail on driver 572.xx — 90-second diagnostic isolates your fix, with version-locked solutions that work.
Apr 18, 2026
OLLAMA_FLASH_ATTENTION=1 set but VRAM stuck? Env var hit systemd, not server. Fix scope, verify 35% savings at 32K context—if GPU's new enough.
Apr 18, 2026
scription: ""File does not exist" at 47%? 73% are disk, path, or permission issues—not remote failures. Map your exact error to the fix in 4 commands
Apr 18, 2026
Two GPUs but only one running? OLLAMA_NUM_GPU=40,40 not 2 — fix layer splits, verify with nvidia-smi, hit 22 tok/s on 70B. Needs Ollama 0.1.38+
Apr 18, 2026
32B Q4_K_M OOM'd? Calculate exact VRAM: weights + KV cache + overhead. 8GB cards hit wall at 13B—here's the quant that fits, with tok/s numbers.
Apr 18, 2026
ROCm won't detect your RX 7900? HSA_OVERRIDE_GFX_VERSION fixes 89% of cases—here's the exact env var, kernel check, and udev rule that works.
Apr 18, 2026
RX 6700 XT not detected by Ollama? One env variable unlocks 38 tok/s—if you pick the right GFX version. Mapping table + syntax for every runtime.
Apr 18, 2026
Ollama falls back to CPU at 3 tok/s. Run 3 commands, apply the NVIDIA/AMD/WSL2 fix, hit 45+ tok/s — but AMD on Windows needs Linux.
Apr 18, 2026
LLM hangs 30s before output? That's prefill, not VRAM. Cut TTFT 60% with 4 diagnostic questions — if CPU-bound, not bandwidth-starved.
Apr 18, 2026
Ollama crashes? NUM_CTX pre-allocates 2.5KB per token on 70B models. Calculate your real VRAM limit, set context right, stop silent CPU fallback.
Apr 18, 2026
Stop Aider calling OpenAI—lock to local Ollama. 16 GB VRAM runs 30B+8B models at 22 tok/s, but 8 GB cards OOM without architect-only mode.
Apr 18, 2026
PDF RAG returns garbage? Fix embedding, chunking, GPU passthrough — 0.89 top-5 accuracy possible, but 8 GB cards force CPU fallback.
Apr 18, 2026
8GB GPUs hit the wall at 14B, 24 GB runs 32B at 18 tok/s — but 70B needs 2 cards or 48 GB unified. Exact VRAM math per quant inside.
Apr 18, 2026
OpenAI SDK fails on local endpoints—fix 3 lines for 35 tok/s inference, but watch the 4K context trap that silent-truncates.
Apr 18, 2026
Alexa sends everything to cloud. This Home Assistant + Ollama pipeline runs 100% local — 2.3s response time, but requires 7B model minimum.
Apr 18, 2026
Your local agent ignores tools or loops forever? Qwen3 14B runs 28 tok/s with 91% tool success — but only if you pull the right Ollama tag. Here's the fix.
Apr 18, 2026
Build a private Copilot in 30 min — Qwen3 14B at 28 tok/s locally with 32k context, but only if you disable Continue.dev's hidden cloud fallback first.
Apr 18, 2026
Stop sending notes to OpenAI—build a private Obsidian AI with local embeddings. 10K notes indexed in 90 min on RTX 3060, but 8 GB GPUs hit the wall.
Apr 18, 2026
Your local LLM is stuck offline. Add web search with this 3-container stack—2.3 GB RAM, 4.2s latency. Catches: version pins matter, CORS breaks silently.
Apr 18, 2026
Cloud automation bills stacking up? Build self-hosted n8n + Ollama workflows for $0 per task — but 8 GB GPUs hit the wall at 7B models. Here's the fix.
Apr 18, 2026
Docker shows No models found and RAG upload spins forever—this guide delivers 45 tok/s local document chat once you fix the 0.0.0.0 bind.
Apr 18, 2026
Stop switching models manually. Auto-route 7B for chat, 70B for code—34 tok/s vs 11 tok/s. Needs 22 GB VRAM, 3 Ollama instances. Here's the YAML.
Apr 18, 2026
AnythingLLM hiding retrieval failures? Build RAG with nomic-embed-text + ChromaDB in 150 lines. 23ms latency, 16 GB VRAM — but chunking breaks precision.
Apr 18, 2026
24 GB GPU crashes with 3+ users? vLLM production setup serves 4 clients at 28 tok/s — only with correct --max-num-seqs. Config inside.
Apr 18, 2026![DeepSeek V4 on Ascend 950PR: Can CUDA GPUs Still Run It? [2026]](/images/deepseek-v4-on-ascend-950pr-can-cuda-gpus-still-run-it/deepseek-v4-on-ascend-950pr-can-cuda-gpus-still-run-it-diagram.jpg)
DeepSeek V4 trained on Huawei Ascend — not NVIDIA. GGUF-quantized V4 still runs on your CUDA GPU. At 1T params, full local runs aren't practical yet.
Apr 16, 2026![Gemma 4 27B on RTX 3090: Q4_K_M Beats Q5 at 8K Context [2026]](/images/gemma-4-27b-on-rtx-3090-q4-k-m-beats-q5-at-8k-context/gemma-4-27b-on-rtx-3090-q4-k-m-beats-q5-at-8k-context-diagram.jpg)
Generic guides get Gemma 4 MoE settings wrong. Exact quant levels, GPU layers, and context limits for RTX 3090 24 GB, from 8K to 128K context.
Apr 16, 2026![Gemma 4 on Laptop: Can Your RTX 4060 Mobile Actually Run It? [2026]](/images/gemma-4-on-laptop-can-your-rtx-4060-mobile-actually-run-it/gemma-4-on-laptop-can-your-rtx-4060-mobile-actually-run-it-diagram.jpg)
Every guide assumes 24 GB desktop VRAM. Take Gemma 4 E2B on RTX 4060 Mobile: exact model picks, settings, and real thermal throttle data.
Apr 16, 2026![llama.cpp 70B on 24 GB VRAM: --n-gpu-layers Guide: A Step-by-Step Guide [2026]](/images/llama-cpp-70b-on-24-gb-vram-n-gpu-layers-guide/llama-cpp-70b-on-24-gb-vram-n-gpu-layers-guide-diagram.jpg)
70B Q4_K_M is 43 GB — won't fit in 24 GB VRAM. Exact --n-gpu-layers settings for RTX 3090 and 4090 hybrid inference. Requires 64 GB+ RAM for 8–12 tok/s.
Apr 16, 2026![LM Studio GPU Not Detected: Every Fix That Works: A Step-by-Step Guide [2026]](/images/lm-studio-gpu-not-detected-every-fix-that-works/lm-studio-gpu-not-detected-every-fix-that-works-diagram.jpg)
LM Studio says no GPU found and the docs don't help. Match your exact error to the right fix in under 5 minutes — works for NVIDIA, AMD, and Intel Arc.
Apr 16, 2026![Ollama Open WebUI RAG Guide: PDF Q&A in 30 Minutes: A Step-by-Step Guide [2026]](/images/ollama-open-webui-rag-guide-pdf-q-a-in-30-minutes/ollama-open-webui-rag-guide-pdf-q-a-in-30-minutes-diagram.jpg)
Most RAG guides skip embedding selection. This one doesn't — Ollama + Open WebUI, PDF ingestion, retrieval tuning, tested on Windows + macOS M4.
Apr 16, 2026![ROCm on WSL2: AMD GPU Setup That Actually Works: A Step-by-Step Guide [2026]](/images/rocm-on-wsl2-amd-gpu-setup-that-actually-works/rocm-on-wsl2-amd-gpu-setup-that-actually-works-diagram.jpg)
ROCm installs break silently and Ollama ignores your AMD GPU — here's the exact version-pinning fix that gets RX 7000 cards running in under 20 minutes.
Apr 16, 2026![Run DeepSeek V3.2 Locally: Hardware Tiers and CPU Offload [2026]](/images/run-deepseek-v3-2-locally-hardware-tiers-and-cpu-offload/run-deepseek-v3-2-locally-hardware-tiers-and-cpu-offload-diagram.jpg)
V3.2's 671B size looks impossible to run locally. Here's the exact quant tier for your VRAM — and the CPU offload path most guides skip.
Apr 16, 2026![CUDA Out of Memory: 12 Fixes Ranked by Success Rate [2026] — diagram](/images/cuda-out-of-memory-fixes/cuda-out-of-memory-fixes-diagram.jpg)
CUDA OOM on local LLM? 12 fixes ranked by how often they work, the Windows VRAM tax most guides skip, and trade-offs documented per fix.
Apr 12, 2026
70B models need 40GB+ VRAM. Four Arc Pro B65 cards stack to 128GB — more than an A100 for less money, if you can stomach the OneAPI software gap.
Apr 12, 2026
VRAM is the only spec that matters — everything else is secondary. Three tiers from $500 RTX 3060 to $2,000+ RTX 3090, matched to model sizes.
Apr 11, 2026
16GB / 24GB unified memory picks. Qwen 2.5 7B/14B remain solid; Qwen 3.6 35B-A3B MoE fits on 24GB with MLX. Picks per RAM tier inside.
Apr 11, 2026
12GB hits its ceiling at 14B Q4_K_M. Qwen 2.5 14B is the proven pick; Qwen 3.6 27B needs --n-cpu-moe offload. Full VRAM fit table inside.
Apr 11, 2026
16GB fits 14B Q4 at full quality. Qwen 2.5 14B at 31 tok/s; Qwen 3.6 35B-A3B with --n-cpu-moe is the new MoE pick. Full VRAM fit table.
Apr 11, 2026
Plan your 2026 AI PC. CPU + GPU + NPU optimization, cost allocation, power budgeting, and motherboard/cooling requirements for local AI inference.
Apr 4, 2026
Compare 6 major open-source LLMs — Qwen, DeepSeek, Kimi, Mistral, Llama Scout/Maverick. Hardware requirements, benchmarks, and decision matrix for your GPU.
Apr 4, 2026
ExLlamaV2 inference optimization. 5.9× speedup over llama.cpp on RTX 5090. Benchmark setup, quantization compatibility, and performance scaling guide.
Apr 4, 2026
Fine-tune 70B LLMs locally with Unsloth LoRA. VRAM requirements, rank selection, and step-by-step guide for RTX 4090 and multi-GPU training.
Apr 4, 2026
Run GLM-4.7 Flash (30B MoE, MIT license) locally. Beats GPT-4 Turbo on SWE-bench. Setup guide with LM Studio, VS Code Copilot, and Cursor integration.
Apr 4, 2026
Run Kimi K2.5 locally on RTX 5090 (single GPU, 45 tok/s). Full hardware requirements, Ollama + Cursor setup, vision integration, and tok/s data.
Apr 4, 2026
Llama 4 Maverick (400B MoE) vs Scout (109B). Real tok/s, hardware costs, and when the bigger model beats the efficient one for local LLM.
Apr 4, 2026
Run Llama 4 Scout 109B locally with Unsloth 1.78-bit quantization. Setup guide for RTX 3090, 10M context integration, and tok/s benchmarks.
Apr 4, 2026
Run MiniMax-M1 1M context on Strix Halo or multi-GPU. 456B MoE architecture, cost analysis, and FLOP efficiency vs DeepSeek R1.
Apr 4, 2026
Run Nemotron 3 Super 120B for local AI agents. Agent framework setup, controllable reasoning budget, and tool integration guide for ReAct and CoT.
Apr 4, 2026
Build offline AI agents with OpenClaw and Ollama. No API calls, full privacy, and local tool integration for automation and code generation.
Apr 4, 2026
Run Qwen 3.5-397B 256K context locally on dual GPU. Tensor parallelism setup, quantization strategy, and inference cost vs Claude API.
Apr 4, 2026
GTT memory expansion on AMD Ryzen AI Max. Kernel parameter guide, stability checks, and model capacity unlock for 72B local inference.
Apr 4, 2026
Two RTX 5090s in an isolated network deliver 27 tok/s on Llama 70B with zero cloud dependency. Here's the $9,000 build for HIPAA-grade local inference.
Apr 2, 2026
Viral dual-GPU 397B builds look impressive—but the tok/s numbers need context. Here's what's real, what's aspirational, and what hardware actually fits.
Apr 1, 2026
Batch size controls how many prompts your GPU processes at once. Learn the exact VRAM cost, throughput tradeoffs, and right settings for each budget tier.
Mar 30, 2026
BF16 uses half the VRAM of FP32 with negligible accuracy loss. Learn which data type to use on RTX 30/40-series GPUs and why the choice matters for your build.
Mar 30, 2026
Bits-per-weight is the spec that determines how much VRAM a model needs. Learn the VRAM cost, quality tradeoffs, and right quantization level for your GPU.
Mar 30, 2026
Decode speed (tok/s) determines how fast your local LLM feels. Learn what drives it, real GPU benchmarks, and why VRAM bandwidth beats TFLOPS every time.
Mar 30, 2026
ExllamaV2 runs 2-3x faster than Ollama on the same GPU using GPTQ/EXL2 models. Real benchmarks, hardware sweet spots, and when the setup overhead is worth it.
Mar 30, 2026
Fine-tuning a 7B model with QLoRA needs 8-10 GB VRAM — not 28-40 GB. Here's the full VRAM math for LoRA, QLoRA, and full fine-tuning by budget tier.
Mar 30, 2026
Flash Attention cuts attention VRAM from ~8 GB to ~1.5 GB at 16K context and speeds up inference 25-40%. Here's what it does and how to enable it in Ollama and vLLM.
Mar 30, 2026
FP16 cuts VRAM by 50% vs FP32 with essentially zero quality loss for inference. Here's the dtype guide for local LLM builders — when to use FP16, BF16, or quantization.
Mar 30, 2026
GDDR6X doubles bandwidth via PAM4 signaling. The RTX 4060 Ti 16 GB gets 28 tok/s while the RTX 4070 12 GB hits 58 tok/s. Here's why GB/s matters more than GB.
Mar 30, 2026
Run local embedding models on your GPU for private semantic search and RAG pipelines. Real VRAM costs, benchmark scores, and Qdrant setup — no API fees required.
Mar 30, 2026
Every rung of the local LLM hardware upgrade path mapped out — from Raspberry Pi curiosity to M5 Max MacBook Pro — with honest numbers and what actually makes you climb.
Mar 21, 2026
Lenovo's dual RTX Pro 6000 workstation will cost $35,000+. Here's how to build the same 192GB VRAM setup for $22,000 — or a rational dual 4090 build for $10,000.
Mar 21, 2026
One GPU isn't enough for 70B models. Here's why the dual-GPU era for local inference has arrived, which pairs make sense, and how to set it all up.
Mar 21, 2026
Persistent memory for your local AI at $0/session — no API needed. Full setup guide: Qwen3-Embedding-0.6B + Qwen3.5-9B + ChromaDB + Ollama on any 16GB GPU, using under 7.3GB VRAM.
Mar 21, 2026
Llamafile 0.10.0 brings CUDA back to the simplest local LLM runtime. Download one file, double-click, run 27B models at 35 tok/s. Here's the full setup guide.
Mar 21, 2026
Mozilla's llamafile 0.10.0 brings back GPU acceleration and a complete llama.cpp rebuild. One file, no install required — here's how to run it in under 5 minutes.
Mar 20, 2026
NVIDIA's Nemotron 4B model runs efficiently on GPUs with just 8GB VRAM. Here's how to set it up locally with Ollama, what it's good at, and how it compares.
Mar 20, 2026
Ollama 0.18.1 ships web search and web fetch as baked-in tools via the OpenClaw agent framework. No RAG pipeline, no Chroma, no SearXNG — just three commands and your local model can query the live web.
Mar 20, 2026
70B models don't need a data center — but they do need VRAM. This guide covers exactly how much you need, which quantization levels work, and which GPUs make it viable.
Mar 19, 2026
Used RTX 4090s at $1,400-1,800 are tempting for 24GB local LLM builds. Here's what to verify before you send money — and what to walk away from.
Mar 12, 2026
Unlock full VRAM headroom on AMD Ryzen AI Max under Linux. Configure GTT memory with kernel parameters to reach up to 108GB for local LLM inference.
Mar 12, 2026
A real $480-520 local LLM build using a used RTX 3060 12GB or RX 6700 XT. What runs well, what doesn't, and honest performance expectations.
Mar 12, 2026
You don't need a $10k GPU to fine-tune a 7B model. With Unsloth and QLoRA, an RTX 3090 or 4090 is enough. This guide walks through the full process from dataset to inference.
Mar 12, 2026
Turn your gaming PC into a local LLM API server with Ollama or LM Studio. Serve OpenAI-compatible endpoints to every device on your home network.
Mar 12, 2026
Practical decision guide to local LLM quantization formats. GGUF runs everywhere, EXL2 is fastest on NVIDIA, AWQ serves vLLM best. Here's exactly which to pick.
Mar 12, 2026
Use huggingface-cli to download GGUF models efficiently. Pick the right quantization, handle gated models, manage the local cache, and organize for Ollama and LM Studio.
Mar 12, 2026
VRAM fills up during long LLM conversations because of the KV cache. Here's how it works, why it grows, and practical fixes to stretch your VRAM further.
Mar 12, 2026
16GB VRAM isn't enough for 70B — but CPU offload changes that. Split layers across GPU and RAM to hit 2–12 tok/s, no upgrade required.
Mar 12, 2026
Step-by-step guide to setting up LM Studio as a local LLM server on your gaming PC. Enable network API, connect other devices, and pick the right model for your VRAM.
Mar 12, 2026
A $1,100-1,300 local LLM build around RTX 4070 or RTX 4060 Ti 16GB. Full parts list, what it runs, and who this build is actually for.
Mar 12, 2026
Real VRAM numbers for Qwen 3.5 9B (6.5GB), 27B (17GB), and 35B-A3B (22GB) at 4-bit — plus Qwen 2.5 72B requirements. Sourced tables, GPU picks per tier.
Mar 12, 2026
Set up vLLM on a single consumer NVIDIA GPU for multi-user OpenAI-compatible API serving. When to choose vLLM over Ollama, installation, and configuration.
Mar 12, 2026
Full component list for a $5,000 workstation-class local LLM build. Dual GPU options, maximum VRAM, and real part picks for serious researchers and developers.
Mar 10, 2026
Apple Intelligence vs self-hosted local LLMs — what Apple Intelligence actually does, why it's not the same as running your own models, and who needs which.
Mar 10, 2026
What CPU offloading is, how the --n-gpu-layers flag works in llama.cpp, and when splitting model layers between VRAM and RAM is worth the speed hit.
Mar 10, 2026
What ECC RAM does, who actually needs it for local LLM workloads, and when it's worth the extra cost. Honest answer for consumer builders and production inference servers.
Mar 10, 2026
Tokens per second tells half the story. This guide covers TTFT, prompt processing speed, llama-bench commands, and how to diagnose when your hardware underperforms its specs.
Mar 10, 2026
The Mac Mini M4 Pro 48GB handles 70B models that PC builds can't without multi-GPU setups. Real token speeds from 7B to 70B — and where the 273 GB/s bandwidth hurts you.
Mar 10, 2026
M4 Max Mac Studio vs RTX 4090 custom PC for local LLM inference. Total cost, performance, and who should choose which — including ecosystem lock-in and resale value.
Mar 10, 2026
M4 Pro is 273 GB/s; M4 Max is 410 or 546 GB/s depending on the GPU. Real bandwidth-based performance numbers for 8B through 70B models, and which config to buy.
Mar 10, 2026
Both run on M-series chips, but they perform very differently depending on model size and task. Here's the real comparison with token speeds across M3/M4 hardware.
Mar 10, 2026
One GPU not enough? Tensor splitting across two cards can unlock 70B model inference at home. Here's how to set it up with llama.cpp and what to expect from the performance.
Mar 10, 2026
Current used market prices, VRAM comparison, and real performance differences between the RTX 3090 and RTX 4090 for local LLM inference in 2026. Which one to buy and when.
Mar 10, 2026
The 9800X3D dominates gaming — but does its 3D V-Cache advantage carry over to LLM inference? Here's how it compares against standard Ryzen and Intel alternatives.
Mar 10, 2026
USB4 and Thunderbolt 4 eGPUs are bandwidth-limited to ~5 GB/s. Here's what that means for LLM inference throughput and whether it's worth trying.
Mar 10, 2026
eBay sourcing guide for used server GPUs including Tesla P40, A100, and H100. Real tradeoffs, risks, and which ones are worth buying for local LLM inference in 2026.
Mar 10, 2026
Running LLaVA, Moondream, and Llama 4 Scout vision models locally on Apple Silicon. Memory requirements, use cases, and honest limitations vs cloud alternatives.
Mar 10, 2026
CPU is irrelevant for pure GPU inference — until you offload 70B layers to RAM. The $200 Ryzen 5 5600 handles it. Here's when upgrading actually matters.
Mar 8, 2026
For CPU inference, RAM bandwidth is the hidden bottleneck. Here's which DDR5 kits actually improve token speeds — and why capacity matters more than frequency for most builds.
Mar 8, 2026
The hardware mistakes that beginners make when building their first local AI setup — and exactly how to avoid each one.
Mar 8, 2026
Does ECC RAM matter for local LLM builds? The real difference between ECC and non-ECC for AI inference, and when the extra cost is worth it.
Mar 8, 2026
Run a shared local LLM that your whole team can access like an internal ChatGPT. Hardware sizing, Ollama vs vLLM, and deployment options covered.
Mar 8, 2026
PCIe x16 vs x8 makes almost no difference once models are in VRAM. Here's when lane count actually bottlenecks your LLM rig — and what to spec for dual or triple GPU builds.
Mar 8, 2026
How to calculate PSU wattage for local LLM builds. Single GPU, dual GPU, efficiency ratings, and specific PSU recommendations for 2026.
Mar 8, 2026
Should you enable XMP or EXPO for local LLM inference? When memory overclocking matters, when it doesn't, and the specific gains you can expect.
Mar 8, 2026
Building from scratch for local AI is different from a gaming build. This guide covers which components actually matter for LLM inference — and which ones you can save on.
Mar 2, 2026
Most gaming cases thermal-throttle a second GPU within minutes. Here's what actually works — slot spacing, airflow path, GPU clearance, and the top picks for 70B multi-GPU rigs.
Mar 1, 2026
Best CPU coolers for 24/7 LLM inference workstations. Air vs AIO, noise levels, and specific recommendations for sustained workloads.
Mar 1, 2026
The right motherboard makes or breaks a multi-GPU LLM build. Here are the best boards for single and dual GPU setups in 2026.
Mar 1, 2026
Your SSD doesn't affect inference speed — but a slow drive adds 60+ seconds every time you load a 40GB model. Here's which drives cut cold-load times without overpaying for PCIe 5.
Mar 1, 2026
How to keep your GPU cool during 24/7 LLM inference. Undervolting, repasting, fan curves, and thermal pad upgrades explained.
Mar 1, 2026
DeepSeek R1 comes in 6 sizes from 7B to 671B. Here's exactly what hardware each variant needs to run locally.
Mar 1, 2026
Gemma 3 27B is one of the most capable open models per VRAM dollar. Here's the minimum GPU, RAM, and quantization settings to run it at usable speeds.
Mar 1, 2026
Mixtral's MoE architecture needs ~26GB VRAM at Q4 — even though only 13B parameters activate per token. Here's the exact hardware to run it and what speeds to expect.
Mar 1, 2026
Run image generation and local LLMs on one machine without constant VRAM juggling. Here's the hardware you need.
Mar 1, 2026
System RAM matters more than you think for local LLMs. Here's how much you need and when faster RAM actually makes a difference.
Mar 1, 2026
The honest answer on whether your 8GB GPU can handle local AI in 2026 — what runs, what doesn't, and when to upgrade.
Mar 1, 2026
Default llama.cpp settings leave 40–60% speed on the table. Master -ngl, -c, tensor-split, mmap, and context tuning to squeeze every token out of your hardware.
Mar 1, 2026
Install LM Studio on Windows, Mac, or Linux, download your first model, enable GPU acceleration, and start running local LLMs without the command line.
Mar 1, 2026
The complete hardware guide for replacing GitHub Copilot with local AI coding assistants. Builds, models, IDE setups, and cost math.
Mar 1, 2026
Mistral Small 3.1 24B fits on a single 24GB GPU at Q4. Here's the best hardware to run it and why this model hits a sweet spot.
Mar 1, 2026
PCIe 3.0 vs 4.0 vs 5.0 NVMe benchmarks for LLM model loading. Tested with 7B, 13B, 30B, and 70B models.
Mar 1, 2026
Install Ollama on Windows, Mac, or Linux and run your first local LLM in minutes. Covers GPU setup, Open WebUI, and model management.
Mar 1, 2026
Exact VRAM requirements and hardware recommendations for running Microsoft's Phi-4 14B locally. Fits in 12GB at Q4.
Mar 1, 2026
Qwen 2.5 Coder 32B punches above its weight for code generation — but it needs ~18GB VRAM at Q4. Here's the hardware breakdown and what speeds to expect on each tier.
Mar 1, 2026
Run two or more local LLMs on a single GPU by managing VRAM, model unloading, and CPU offloading. Practical limits explained.
Mar 1, 2026
Buy for the model, not the benchmark. This guide maps each hardware tier to what it actually runs — 7B to 70B+, from $220 used cards to Apple Silicon — with real speed benchmarks.
Feb 28, 2026
Step-by-step guide to running AI models on your own hardware. Covers Ollama setup, model selection, hardware requirements, and getting your first model running in under 10 minutes.
Feb 28, 2026
What can you actually run locally at $200, $400, $600, and $1,000+? Honest breakdown of every budget tier with real hardware recommendations and what you're giving up.
Feb 28, 2026
A systematic approach to choosing local AI hardware. Answer 5 questions, get a clear recommendation. No wasted money on specs that don't matter for your use case.
Feb 28, 2026
Apple Silicon vs NVIDIA GPU for running local LLMs — which is actually better? Real benchmarks, use cases, and the honest answer based on what you need.
Feb 28, 2026
A dual-GPU PC build is the most cost-effective way to run 70B models at desktop speed. Two used RTX 3090s with NVLink gives you 48GB combined VRAM for under $3,000.
Feb 27, 2026
You need an M4 Max or M3 Ultra Mac with at least 128 GB to run Llama 3 70B comfortably. Best setup is MLX through LM Studio — expect ~11-12 tok/s at Q4, which is conversational speed.
Feb 27, 2026
You can run Llama 3 locally for as little as $250. Here's what each price tier gets you — and honest expectations about how it compares to ChatGPT.
Feb 25, 2026
VRAM is the single biggest constraint for running local LLMs. Here's exactly how much you need for every model size — and what happens when you don't have enough.
Feb 25, 2026