How many tokens per second does the EVO X2 get on Llama 70B?

Real-world testing shows approximately 3–13 tokens per second depending on quantization and workload. Llama 70B Q8 achieves ~3 tok/s, while DeepSeek R1 Distilled (70B) reaches ~13 tok/s. This is practical for interactive use only on smaller models (8B–32B).

Is the EVO X2 better than an RTX 4080 SUPER for local AI?

For raw inference speed: no. RTX 4080 SUPER dominates on throughput (~45–55 tok/s on 70B, if it fit in 20GB VRAM). EVO X2 wins on flexibility—96GB unified memory, zero driver headaches, and silent operation. Pick RTX 4080 for speed; pick EVO X2 for peace and multi-model serving.

Does unified memory actually eliminate PCIe bottlenecks?

Yes, unified memory removes the GPU-to-CPU transfer penalty that discrete GPUs face. This helps on workloads that shuffle data frequently (RAG, multi-model inference, dynamic batching). For pure generation speed on a single model, GPU compute is still the bottleneck, not memory bandwidth.

What's the price—is it really $1,499?

Promotional pricing starts at $1,499 (64GB+1TB sale), but list prices are $2,199–$2,799 depending on config. The 96GB+2TB model lists at $2,599 MSRP, often discounted to $1,799. Check current pricing on Amazon or GMKtec before buying.

GMKtec EVO X2 Review: 96GB Unified Memory for Local LLMs [Honest Verdict]

Name: GMKtec EVO X2 Review: 96GB Unified Memory for Local LLMs [Honest Verdict]
Item: GMKtec EVO X2
Author: Ellie Garcia

Some links on this page may be affiliate links. We disclose it because you deserve to know, not because it changes anything. Every recommendation here comes from benchmarks, not budgets.

The Uncomfortable Truth About the EVO X2

The GMKtec EVO X2 is a polished prebuilt with 96GB of unified memory and a fanbase that oversells its inference speed. YouTube reviewers claim it rivals the RTX 4090. AMD's marketing says 2.2× faster than RTX 4090. The reality? Llama 3.1 70B runs at 3 tokens per second.

That's slower than most people want to wait. So here's the honest take: the EVO X2 isn't a discrete GPU killer. It's a different tool—one that trades raw throughput for silence, simplicity, and the unique power of unified memory architecture. If you value that trade-off, it's worth every penny. If you need speed, it's not.

Why This Matters

The EVO X2 launches at a critical inflection point in local AI. For the first time, you can load a 70B model on consumer hardware without a $2,000+ GPU. The catch: you can load it, but you'll run it slowly. The why is important—and it's not a flaw, it's by design.

Unified memory is not VRAM. VRAM is optimized for the compute patterns GPUs excel at (parallel matrix operations). Unified memory is optimized for sharing data efficiently between CPU and GPU. The Ryzen AI Max 395's iGPU is a high-end mobile chip, not a server accelerator. Asking it to sustain 50 tok/s on 70B is like asking a gaming laptop GPU to match a Tesla H100. Wrong tool.

Where the EVO X2 actually wins: model flexibility, multi-model switching without cold starts, and practical inference on 8B–32B models where it achieves 35–50 tok/s—respectable territory.

GMKtec EVO X2 Specs — Where the Magic Actually Happens

Spec	Value
CPU	AMD Ryzen AI Max+ 395 (16-core Zen 5, 32 threads, up to 5.1 GHz)
iGPU	Radeon 8060S (RDNA 3.5, 40 compute units / 2,560 shaders)
Memory (GPU)	Up to 96GB LPDDR5X-8000 unified (config-dependent)
Memory Bandwidth	256 GB/s (LPDDR5X at 8000 MT/s on 256-bit bus)
TDP	120W sustained, ~140W short burst
Storage	1TB or 2TB PCIe 4.0 NVMe SSD
Connectivity	WiFi 7, USB 4, Dual Thunderbolt, microSD reader
Cooling	Active (triple-fan system, adjustable, ~35 dB quiet mode)
Price	$1,799–$1,999 (promotional) / $2,199–$2,799 MSRP

The memory bandwidth figure is the headline: 256 GB/s compares favorably to some discrete GPUs. But bandwidth ≠ compute. A RTX 4080 has lower peak bandwidth (~576 GB/s) but 20x the streaming multiprocessors dedicated to math operations.

Note

Unified memory means the GPU doesn't need separate VRAM. It accesses system RAM directly. This eliminates PCIe bottlenecks for CPU-GPU transfers—critical for RAG systems and dynamic workloads. For static model loading and generation, compute is still your constraint.

Real-World Inference Benchmarks

These are community-tested results on stock configs, using llama.cpp and LM Studio. Numbers from ServeTheHome and community forums.

Llama 3.1 Inference

Notes

Practical, responsive

Still usable

Slower but accurate

Not recommended for interactive use

Theoretical; most users skip this

Specialized Models

DeepSeek R1 Distilled (70B): ~13 tok/s in LM Studio with Windows 256GB allocation (as of March 2026)
Qwen 32B Q4_K_M: ~12–14 tok/s (practical for coding assistance)
Mistral Large (32B): ~16–18 tok/s

The pattern: anything under 32B runs decently. Anything over 70B runs so slowly that you're better off with a smaller model running 3–4× faster.

Warning

AMD's claim of "2.2× faster than RTX 4090" refers to MoE (Mixture of Experts) models under specific conditions, NOT dense 70B models. Dense 70B inference is where the EVO X2 underperforms relative to discrete GPUs.

Who Should Buy the EVO X2?

✅ Buy If...

You hate GPU driver hell. AMD GPU drivers for inference are fragile on Windows. EVO X2 uses CPU dispatch or integrated GPU paths that just work. No kernel panics, no ROCm version mismatches.
You need silence. 35 dB in quiet mode beats any GPU rig. Perfect for home studios, quiet offices, or library setups.
You're running 8B–32B models daily. At 35–50 tok/s on these sizes, the EVO X2 is practical and pleasant to use.
You want to prototype multi-model systems. Load Llama 8B, Qwen 32B, and a vision model simultaneously. Unified memory handles this elegantly. Discrete GPUs would require careful offloading or multiple cards.
RAG or dynamic workloads. Unified memory shines when CPU and GPU swap data frequently (retrieval, re-ranking, token streaming).

⏸️ Wait If...

Ryzen AI Max+ Gen 2 is 6–9 months out with rumored +15–20% perf bump (unconfirmed as of April 2026). If you can wait, new models often drop prices on previous gen.
You're on a strict budget. Promotional pricing of $1,799 is good, but $1,200–$1,400 gaming PC + used RTX 4070 is still cheaper for equivalent VRAM capacity.

❌ Skip If...

You need high throughput. 3 tok/s on 70B is frustrating. If you need 30+ tok/s consistently, a discrete RTX 4080 ($999 MSRP) or RTX 5070 Ti ($749) is the better move.
You're building a production AI service. Single-machine prebuilts are hard to scale. Go discrete GPU for parallelization.
You game + AI on the same rig. The iGPU can't context-switch between games and inference. Discrete GPU gives you true multitasking.

EVO X2 vs RTX 4080 SUPER — The Real Showdown

This is the comparison that matters if you're deciding between $1,800 today.

RTX 4080 SUPER

$999 MSRP / ~$1,100–$1,500 current market

Requires CPU, mobo, RAM, PSU, case (~$500–$700 total)

~25–35 tok/s (hybrid, since 70B doesn't fit in 20GB VRAM)

20GB discrete VRAM

~70–85 dB under full load

~320W GPU + CPU

Active, often loud

One model per GPU, careful offloading

Possible (AMD ROCm or NVIDIA CUDA)

The Verdict on This Matchup

If you prioritize speed: RTX 4080 SUPER wins decisively. For $1,500 in total hardware (GPU + system), you get 3–4× the tokens/second. The noise and driver complexity are the trade-off.

If you prioritize silence + flexibility: EVO X2 wins. You get a polished, silent machine that loads bigger models than a 4080 can and doesn't require driver troubleshooting. You sacrifice speed—accept it before you buy.

Tip

Real-world decision tree: If "tokens per second" is your primary metric, pick discrete GPU. If "I want to run a 32B model quietly without driver hell" is your priority, pick EVO X2. They solve different problems.

EVO X2 vs Mac Studio M4 Max — The Unified Memory Kings

Both systems use unified memory, but the M4 Max is a different beast entirely.

Mac Studio M4 Max

$1,999–$3,999

32GB–128GB (M4 Max)

~15–25 tok/s

macOS, MLX, Ollama

~25 dB (passively cooled) Winner: Depends on your ecosystem. If you're deep in Apple/macOS development, the M4 Max is worth the premium. If you want Windows/Linux flexibility at lower cost, EVO X2 wins.

Final Verdict — Buy, Wait, or Skip?

The Bottom Line

The GMKtec EVO X2 is a solid buy at $1,799–$1,999 if you want to run 8B–32B models in complete silence without driver worries. It's not a GPU killer, and that's okay. It's a different category—and it owns that category.

At $2,599 MSRP, it's overpriced. Wait for sales or pick a discrete GPU instead.

For Budget Builders: If you have $1,800 to spend, the EVO X2 gets you to 8B models quietly. A $1,200 GPU build gets you to 70B models at speed. Know which matters to you.

For Power Users: The unified memory architecture is genuinely novel. If you're building RAG systems or need multi-model serving, EVO X2 is worth exploring despite the speed limitations.

For Speed Chases: RTX 5070 Ti at $749 still crushes this at inference throughput.

Our Rating

Notes

Good at promotional pricing, overpriced at MSRP

Excellent for 8B–32B, weak on 70B+

True silent computing

Zero driver hassle, prebuilt ready

Unified memory shines here

Solid niche play, not a general recommendation

FAQ

Can the EVO X2 really run 70B models locally?

Yes, technically. Practically? No. At 3 tok/s, you'll wait 3–5 seconds per token for response. For interactive use, anything above 32B is frustrating. Use smaller models or accept multi-second latencies.

Is the $1,799 price the real price?

No. $1,799 is a promotional discount. MSRP on the 96GB+2TB model is $2,599. Prices fluctuate; check current retail before committing.

Does unified memory really matter for inference?

Yes—but not the way marketing suggests. Unified memory eliminates PCIe transfers, which matters for RAG and multi-model workloads. For raw generation speed on a single model, compute (GPU cores) is still the bottleneck.

Should I get this or a gaming GPU?

Gaming GPU (RTX 4060 Ti, RTX 5070 Ti) if you care about tokens/second and don't mind noise. EVO X2 if silence and ease of use matter more than speed.

What about the NPU?

The Ryzen AI Max+ has a dedicated NPU, but llama.cpp doesn't use it yet. Future software may unlock it, but don't count on it today.

Is this better than a Mac Mini?

Mac Mini doesn't ship with M4 Max—that's Mac Studio territory at $1,999+. If comparing to Mac Studio, EVO X2 is cheaper and more flexible. Mac Studio has better single-threaded performance and a more mature AI software ecosystem.

Tested by: Ellie Garcia
Last verified: April 3, 2026
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