Best Chinese Open-Source LLMs to Run Locally in 2026

Most local AI builders are still running Llama 3.1 and Mistral. That was the right call in 2024. In 2026, it's leaving 15-20% reasoning performance on the table — for free.

DeepSeek-R1-Distill-Llama-70B scores 86.7% on AIME 2024 math competition problems. Qwen 2.5 72B runs production language tasks at zero API cost under Apache 2.0. InternLM 2.5 7B screams at 60+ tok/s on hardware that costs under $430. Chinese open-source models now cover every tier from lightweight edge inference to frontier-class reasoning — and the GPU you already own probably runs at least one of them.

The catch: most guides get the hardware requirements badly wrong. This one doesn't.

Why Chinese Open-Source Models Matter in 2026

A year ago, the default local LLM conversation started and ended with Llama 3.1 70B. That model set the bar for open-source reasoning. By late 2025, Chinese labs cleared it.

DeepSeek-R1-Distill-Llama-70B outperforms Llama 3.1 70B on AIME math benchmarks (86.7% vs. ~65%), uses MIT licensing, and distributes through Hugging Face and Ollama. Qwen 2.5 72B publishes under Apache 2.0 — full commercial use, no restrictions. These aren't research projects or limited preview releases. They're production-ready open-weights models that install in one command.

The financial case is straightforward. Running DeepSeek-R1-Distill locally costs $0/month after your GPU purchase. GPT-4o currently prices at $2.50 per million input tokens and $10 per million output tokens (as of March 2026). At moderate usage — 1-3 million tokens per month — you're paying $50-200/month for API access. A single RTX 5090 at $1,999 MSRP pays back that cost in 10-40 months of use. If you're a heavy user or running a small business, the math closes faster.

The other change is licensing. A year ago, most competitive Chinese models were API-only. Now, every model covered in this guide ships with open weights and commercial-friendly licenses. You download them, you own them, they never call home.

Quick Picks: Budget / Mid-Range / High-End

Notes

Comfortable fit, 35+ tok/s

Tight but functional

Full reasoning, single card

Maximum quality Prices as of March 2026 — MSRP. Street prices are running 10-40% above MSRP due to supply shortages.

Model Reviews

DeepSeek-R1-Distill-Llama-70B — The Reasoning Champion

The strongest Chinese open-source model you can run locally — if you have the hardware.

AIME 2024 score: 86.7% (source: official model card, verified March 2026). That's competitive with GPT-4-class performance on competition-level math problems. For complex code generation, STEM research, and multi-step decision workflows where accuracy matters more than speed, nothing in the open-source catalog at this scale does it better.

The honest hardware reality: the Q4_K_M GGUF build weighs approximately 42.8 GB. The RTX 5090's 32 GB is still short for Q4. You can run Q3_K_M (~28 GB) on a single RTX 5090 with a comfortable margin — at a roughly 4-8% accuracy tradeoff. Dual RTX 5090 at Q4 delivers around 27 tok/s based on benchmarks from databasemart.com (as of early 2026). Single RTX 4090 with CPU offload: 5-15 tok/s depending on context length and RAM speed.

For most builders on a single GPU, the 32B distill below is the right starting point.

Best for: Hard math, STEM research, complex code debugging, decision systems where a wrong first answer is costly.

DeepSeek-R1-Distill-Qwen-32B — The Practical Sweet Spot

If you want R1-class reasoning on a single consumer GPU, start here — not with the 70B.

AIME 2024: 72.6% (source: official DeepSeek-R1 technical report, January 2025). MATH-500: 94.3%. That 72.6% puts it ahead of OpenAI's o1-mini on math benchmarks — a significant data point for a model that fits in 17 GB at Q3_K_M. On an RTX 5070 Ti (16 GB), it runs with a narrow margin. On an RTX 5090 at Q4_K_M (~22 GB), it runs with headroom and delivers noticeably sharper reasoning.

The speed profile is also honest: 10-14 tok/s at Q3 on 16 GB hardware. Fast enough for interactive coding sessions, slow enough to be thoughtful about which tasks you send it.

Best for: Daily coding assistance, reasoning-heavy workflows, anyone upgrading from Llama 3.1 70B who wants genuine benchmark improvement without a multi-GPU rig.

Qwen 2.5 72B — The Versatile Workhorse

Where DeepSeek-R1 specializes in deep reasoning chains, Qwen 2.5 72B Instruct is the model you'd actually run all day across mixed tasks — writing, coding, translation, analysis, summarization.

LiveCodeBench score: 55.5 (problems 2305-2409, source: official Qwen blog, verified March 2026). Competitive for a 72B open-source model. Apache 2.0 license, native Ollama support, broadly deployed in production setups.

The hardware reality matches the DeepSeek-R1 70B situation: Q4_K_M runs approximately 43 GB. A single RTX 5090 (32 GB) handles Q3_K_M with some margin. For 16 GB cards, you're either accepting CPU offloading (1-3 tok/s, barely interactive) or running the Qwen 2.5 32B variant instead.

One common claim to correct: Qwen 2.5 72B is not more compressible than DeepSeek-R1-Distill-Llama-70B at the same quantization level. The DeepSeek distill Q4_K_M (~42.8 GB) is slightly smaller than the Qwen 2.5 72B Q4_K_M (~43 GB). The difference is marginal, but the direction matters.

Best for: Mixed daily workloads — coding, writing, translation, analysis — where you need consistent multi-task performance over pure reasoning depth.

Yi 1.5 34B — The Mid-Range Stepping Stone

01.AI's Yi 1.5 34B punches above its parameter count on general reasoning tasks. It's not DeepSeek-R1-Distill-32B on math benchmarks, but for code generation, content writing, and classification, it performs solidly.

Hardware reality: At Q4_K_M, a 34B model requires approximately 22-24 GB VRAM. That puts it just out of reach for 16 GB cards without CPU offloading — and CPU offloading for this model drops to 3-5 tok/s, which isn't usable for interactive work. An RTX 5090 (32 GB) runs it at Q4 with headroom at roughly 20-25 tok/s.

If you're on a 16 GB card and want a 30B+ Chinese model, the R1-Distill-32B at Q3_K_M is the better play — stronger reasoning, similar size with quantization, and verifiably sourced benchmark scores.

Best for: RTX 5090 owners who want a versatile 34B model for diverse tasks alongside a lighter 7B-14B model for high-volume inference. See our GPU selection guide for 2026 for full pairing recommendations.

InternLM 2.5 7B — The Lightweight Foundation

The right model when you need volume, not depth. At Q4_K_M quantization, InternLM 2.5 7B fits in approximately 5-6 GB VRAM — which means it runs on the base RTX 5060 Ti 8 GB variant, an M3 MacBook Pro, or virtually any modern GPU.

Expect 60+ tok/s on mid-range GPUs. On an M3 MacBook Pro, it runs at ~40 tok/s without the fans spinning up. For classification, summarization, basic coding assistance, and any use case where latency matters more than reasoning depth, that speed profile is exactly what you want.

It's not an AIME-level reasoning model. Don't use it for hard math or complex multi-step debugging. Use it for the 80% of daily AI tasks that don't need deep reasoning, and save the 32B/70B models for when you actually need them.

Best for: High-volume inference, edge deployment, budget builds under $430, or as a fast sidekick running in parallel with a larger model.

Hardware Matching: What Actually Runs Where

Dual RTX 5090 64 GB

✓ Q8

✓ Q8

✓ Q5

✓ Q5

✓ Q4 (~22 tok/s)

✓ Q4 (~27 tok/s) Tok/s estimates from databasemart.com benchmarks (RTX 5090 dual), Ollama community tests, and GGUF file sizes from bartowski's Hugging Face repos. Exact speeds vary by context length, backend version, and system RAM. Last verified: March 2026.

Distributed Inference for 70B Models

Dual RTX 5090 with vLLM tensor parallelism delivers near-linear scaling for 70B models — approximately 27 tok/s at Q4 versus ~10 tok/s on a single RTX 5090 at Q3. The PCIe bandwidth penalty versus NVLink-connected server GPUs costs roughly 20-25% of theoretical maximum, but it's still a significant real-world jump. For a full walk-through, see our dual GPU local LLM stack guide.

Quantization: Which Level to Use

Quantization compresses model weights. Every level trades accuracy for VRAM and speed. Here's the practical breakdown across the 70B-class models:

Recommendation

Avoid for reasoning tasks

Acceptable for casual use

Sweet spot for RTX 5090 single-card

Standard — use if VRAM allows

Multi-GPU or server GPU only For 32B models, shift each VRAM value down by ~40%. Q4_K_M for a 32B model runs ~22 GB; Q3_K_M fits in ~17 GB, making it viable on 16 GB consumer cards.

Benchmark Comparison

All scores below are from official model cards and published benchmark reports. Test methodology noted.

Source

HuggingFace model card

DeepSeek-R1 technical report

qwenlm.github.io official blog

Limited third-party data

Limited third-party data Note: Benchmark scores reflect full-precision or near-full inference conditions. Locally quantized GGUF builds at Q3-Q4 typically run 2-5% below these published figures. Yi and InternLM scores are estimates from community testing — treat as directional, not precise.

Workflow Matching

Coding and STEM Research

Best choice: R1-Distill-Qwen-32B on a 16 GB card, or R1-Distill-Llama-70B on an RTX 5090.

The R1 reasoning chain genuinely helps with multi-step debugging — not just autocomplete, but working through why a function is broken across multiple layers of logic. The 32B distill handles roughly 90% of this at a fraction of the hardware cost. For comparing this against Llama-family models, the reasoning gap is most visible on problems that require holding multiple constraints simultaneously.

High-Volume General Inference (SaaS, Chatbots)

Best choice: Qwen 2.5 32B on an RTX 5090, or R1-Distill-14B on 16 GB hardware.

Apache 2.0, broadly tested in production, native Ollama support. For sustained multi-task inference — diverse query types, long sessions, variable context — Qwen's general-purpose tuning outperforms the reasoning-specialized R1 distill variants on average latency.

Budget-First Build ($429 GPU)

Best choice: R1-Distill-Qwen-14B on RTX 5060 Ti 16 GB ($429 MSRP).

A 14B distill at 35+ tok/s beats a 70B model at 3 tok/s over CPU offload every time for interactive use. Get the smaller model, run it fast. If you later hit a reasoning ceiling, the path forward is a VRAM upgrade — not a different model.

Edge or Low-Power Deployment

Best choice: InternLM 2.5 7B or R1-Distill-14B.

InternLM 2.5 runs on a MacBook Pro M3 at ~40 tok/s without fan noise. The R1-Distill-14B is quieter than you'd expect for a reasoning model at that size. Both fit in 8-10 GB VRAM at Q4. For the full Ollama installation process, the Ollama setup guide for local LLM walks you through everything from download to API access.

How to Run These Models

Ollama (Simplest Path)

Ollama handles CUDA drivers, quantization selection, and memory management automatically.

# DeepSeek-R1 Distill 32B
ollama pull deepseek-r1:32b

# Qwen 2.5 72B (requires RTX 5090 or dual GPU at Q3)
ollama pull qwen2.5:72b

# Yi 1.5 34B (requires RTX 5090 at Q4)
ollama pull yi:34b

# InternLM 2.5 7B
ollama pull internlm2:7b

Run ollama run <modelname> for a CLI session or hit localhost:11434 for API access.

vLLM for Multi-GPU Setups

pip install vllm

# Run DeepSeek-R1 70B across two GPUs
python -m vllm.entrypoints.openai.api_server \
  --model deepseek-ai/DeepSeek-R1-Distill-Llama-70B \
  --tensor-parallel-size 2

The --tensor-parallel-size 2 flag splits the model across both GPUs with near-linear scaling. This is how you get from ~10 tok/s on a single RTX 5090 to ~27 tok/s on dual RTX 5090 for the 70B variant.

CraftRigs Verdict

This is a hardware decision first. Match your GPU to a model that fits — then pick the best Chinese model in that tier.

$429 GPU (RTX 5060 Ti 16 GB): DeepSeek-R1-Distill-Qwen-14B at Q4. Around 35 tok/s of genuine R1-class reasoning that handles most coding and analysis tasks. No CPU offloading required.

$749 GPU (RTX 5070 Ti 16 GB): R1-Distill-Qwen-32B at Q3_K_M. You'll get 72.6% AIME reasoning at 10-14 tok/s. The quality reduction from Q4 is imperceptible for 99% of real tasks. This is the "no wrong choice" tier — you get meaningful reasoning depth without multi-GPU complexity.

$999 GPU (RTX 5080 16 GB): Same models as the 5070 Ti tier, but with more CUDA throughput — expect roughly 30-40% more tok/s on the same quantized models. Same 16 GB VRAM ceiling applies.

$1,999 GPU (RTX 5090 32 GB): R1-Distill-Llama-70B at Q3_K_M. You get the full 86.7% AIME reasoning capability on a single card. Yi 1.5 34B at Q4 also fits here with headroom. This is where the Chinese model advantage over Western open-source becomes undeniable.

$4,000+ (Dual RTX 5090): Full Q4 for any model in this guide. Maximum quality, maximum speed. Justified if your work lives in complex code, hard math, or research where model accuracy directly affects output quality.

All of these run completely offline, zero data leaving your machine. That alone justifies the hardware cost for any work where you can't afford to send queries to an external API.

FAQ

Can I run DeepSeek-R1 70B on an RTX 5080?

No — the RTX 5080 has 16 GB VRAM. DeepSeek-R1-Distill-Llama-70B at Q4_K_M requires ~42.8 GB. Running with CPU offloading drops speeds to 2-5 tok/s, which isn't practical for interactive use. The DeepSeek-R1-Distill-Qwen-32B at Q3_K_M (~17 GB) is the correct single-card recommendation for 16 GB hardware. For the 70B model specifically, you need an RTX 5090 (32 GB, runs Q3) or dual RTX 5090 (runs Q4).

Is DeepSeek-R1 better than GPT-4 for math and coding?

On AIME 2024 competition problems, the 70B distill scores 86.7% — GPT-4 range. It genuinely solves hard math problems that smaller open-source models get wrong. For day-to-day coding assistance, the 32B distill at 72.6% AIME is nearly as capable. Neither replaces GPT-4o for multimodal tasks, but for pure text-based reasoning and code generation running locally and privately, DeepSeek-R1 distill is the best open-source option available.

What's the best Chinese LLM for a $749 GPU budget?

R1-Distill-Qwen-32B at Q3_K_M on an RTX 5070 Ti ($749 MSRP). Fits in 16 GB at Q3 with ~3-4% accuracy reduction versus Q4. Qwen 2.5 32B is the alternative if you need Apache 2.0 licensing and multi-task versatility over pure reasoning depth.

Do I need a multi-GPU setup to run 70B models?

For GPU-only inference at Q4 on consumer hardware: yes. The RTX 5090 (32 GB) runs the 70B distill at Q3_K_M without offloading. Dual RTX 5090 handles Q4 at ~27 tok/s. If you want to avoid the cost, the 32B distill at Q4 on a single RTX 5090 delivers very strong reasoning at much lower hardware cost.

Does Kimi K1.5 have open weights for local use?

No — Kimi K1.5 (January 2025) was API-only. Moonshot AI's first openly licensed model is Kimi K2 (MIT license, July 2025), a large mixture-of-experts model. K2 GGUF builds exist, but it's a very large model outside practical single-GPU consumer territory. If you want strong math performance locally, DeepSeek-R1-Distill-Llama-70B remains the more practical choice.

Prices as of March 2026. Benchmarks last verified: March 29, 2026.