Best GPU for Mistral Models in 2026 (5 Picks Ranked)

From the Best GPU for LLM archive. The canonical version has interactive calculators, an up-to-date GPU comparison table, and live pricing.

Mistral 7B is one of the most efficient models for its quality tier — and it runs beautifully on budget hardware. For Mistral 7B, the RTX 4060 Ti 16GB at $400 is all you need. Mixtral 8x7B is a different proposition: the MoE architecture gives 13B-class quality but demands 26GB+ of VRAM, making the RTX 5090 the only single-card consumer option.

See the recommended pick on the original guide

Mistral model family overview

Mistral AI has shipped several distinct model architectures, each with different VRAM demands:

The lineup spans from accessible (7B on any GPU) to cloud-only (Large, 8x22B). Mixtral 8x7B is the MoE model that draws most attention — it delivers near-13B-quality output at near-7B inference speed, but only if you can fit all 46.7B parameter weights in VRAM.

VRAM chart available at the original article

Understanding Mixtral's MoE architecture

Mixtral 8x7B confuses many first-time buyers. A quick breakdown:

• 8 expert networks, each with 7B parameters
• 2 experts activate per token at inference time (12.9B active parameters)
• All 46.7B parameters must be loaded in VRAM at all times

This means Mixtral feels like running a 13B model in terms of speed and output quality, but requires VRAM for all 46.7B parameters simultaneously. You get the quality without paying the compute cost — but you pay the full memory cost.

The practical implication: Mixtral needs a 32GB card, despite feeling "like a 13B model."

Best GPUs for Mistral 7B

Mistral 7B is one of the best models to run on a budget. It punches well above its weight class on coding, instruction-following, and structured output tasks.

At Q4_K_M, Mistral 7B uses only about 4.5GB of VRAM. Even the RTX 3060 12GB handles it with 7.5GB to spare for context. The RTX 4060 Ti 16GB can run Mistral 7B at FP16 — full precision, maximum quality, no quantization — with about 2GB to spare for context.

FP16 Mistral 7B on a 16GB card is one of the highest quality-per-dollar local setups available.

Optimal quantization for Mistral 7B per GPU

Mistral 7B at FP16 on a 16GB card is one of the few practical use cases where full precision is achievable on affordable hardware. Most 7B models are better run at Q8 to save VRAM for context — but Mistral's small FP16 footprint (~14GB) fits on 16GB cards with modest context.

Best GPUs for Mixtral 8x7B

Mixtral is where GPU selection becomes critical. At Q4_K_M (~26GB), it exceeds every consumer GPU except the RTX 5090:

The RTX 5090 is the only single consumer GPU that fits Mixtral 8x7B at Q4_K_M with room for context. The RTX 4090 can run it at Q3_K_M (~20GB) or Q3_K_S (~18GB) with acceptable quality — the MoE architecture is somewhat more tolerant of quantization than equivalent dense models. Dual RTX 4090s give you 48GB for Q4 quality.

Mixtral 8x7B vs Mistral 7B: when to use which?

For everyday tasks and fast responses, Mistral 7B wins on accessibility. For tasks where output quality matters and you have a 32GB card, Mixtral delivers meaningfully better results at nearly the same inference speed.

Mistral Small 22B: the overlooked middle option

Mistral Small 22B is a dense 22B model that runs at ~13GB at Q4_K_M — fitting on a 16GB GPU. It offers quality that falls between 7B and Mixtral 8x7B, with inference speed comparable to Llama 2 13B.

If you have 16GB VRAM and want more quality than 7B without a 32GB card for Mixtral, Mistral Small 22B is worth considering.

Ollama compatibility for Mistral models

All Mistral models work with Ollama via GGUF quantization:

# Mistral 7B
ollama run mistral

# Mixtral 8x7B (needs 32GB VRAM)
ollama run mixtral

# Mistral Small 22B
ollama run mistral-small

Ollama automatically handles GGUF conversion and GPU offloading. For Mixtral on a 24GB card, Ollama will attempt to offload some layers to CPU, resulting in very slow inference. For Mixtral, either a 32GB card or dual 24GB cards is required for practical speeds.

Mistral Large: cloud territory

Mistral Large at 123B parameters requires ~70GB at Q4_K_M. This is firmly multi-GPU or cloud territory. If you need Mistral Large locally, budget for 2–3x RTX 4090s or look at workstation cards like the RTX 6000 Ada (48GB). For most users, Mixtral 8x7B at Q4 or Mistral Small 22B delivers most of the quality improvement at a fraction of the hardware cost.

Which GPU should you buy for Mistral?

Running Mistral 7B for chat, coding, or general tasks? → RTX 4060 Ti 16GB ($400). 16GB VRAM lets you run at FP16 full precision — the best quality any 7B model can deliver. No quantization needed.

Want a budget option for Mistral 7B only? → RTX 3060 12GB used ($250). Handles Mistral 7B at Q6_K or Q8 with fast inference (~30 tok/s). Excellent value for 7B-only use.

Running Mixtral 8x7B (single GPU)? → RTX 5090 ($2,000). The only single consumer GPU with 32GB to fit Mixtral's 26GB Q4_K_M weight cleanly.

Running Mixtral 8x7B at best quality? → 2x RTX 4090 ($3,200). 48GB combined gives headroom for Q5_K_M or Q6_K and long context windows.

On a 24GB budget for Mixtral? → RTX 4090 ($1,600) with Q3_K_M. Lower quality than Q4 but workable. The MoE architecture handles quantization better than comparable dense models.

Memory bandwidth and Mistral inference speed

Mistral 7B's small weight footprint means inference speed is almost entirely determined by memory bandwidth — not compute. Higher bandwidth equals faster tokens per second:

The RTX 3090's 936 GB/s bandwidth makes it one of the fastest inference cards for small models despite being a previous generation. A used 3090 at ~$900 generates Mistral 7B tokens at the same speed as an RTX 4090 for a fraction of the price.

Common mistakes to avoid

• Assuming Mixtral needs less VRAM because of MoE — only 12.9B parameters are active per token, but all 46.7B must be in VRAM simultaneously. You pay the full memory cost.
• Buying 8GB VRAM for Mistral 7B — Mistral technically fits at Q4 in 8GB, but you lose the ability to run FP16 and have minimal context headroom. 16GB unlocks the full model potential.
• Ignoring memory bandwidth for small models — Mistral 7B's speed scales with bandwidth, not compute. An RTX 3090 (936 GB/s) generates tokens 2x faster than an RTX 4060 Ti (288 GB/s) at the same model.
• Choosing AMD without checking Mistral compatibility — Mistral 7B works via llama.cpp Vulkan, but Mixtral MoE inference on ROCm has known performance issues. Stick with NVIDIA for Mixtral.
• Overlooking Mistral Small 22B — this 22B dense model fits on a 16GB GPU at Q4 and delivers meaningfully better quality than 7B for users who cannot afford a 32GB card for Mixtral.

Our recommendation

Mistral 7B at FP16 on an RTX 4060 Ti 16GB is one of the best value local LLM setups available — full precision, ~28 tok/s, and the most capable 7B architecture. For Mixtral, the RTX 5090 is the clear single-card winner.

See the recommended pick on the original guide

See the recommended pick on the original guide

See the recommended pick on the original guide

If you use Ollama for model management, these GPU picks apply directly — Ollama handles GGUF quantization and GPU offloading automatically. For broader budget comparisons, see the best budget GPU for local LLM guide.

Related guides on Best GPU for LLM

• Best Budget GPU for Local LLM 2026: RTX 3060 to $350
• Best GPU for Continue.dev (Local AI Coding) in 2026
• Best GPU for Gemma 2B-27B in 2026 (6 Picks Ranked)

Read the full guide on Best GPU for LLM — includes our VRAM calculator, GPU comparison table, and live pricing.