Can the RTX 4060 Run Mistral 7B?
Runs
Yes. Mistral 7B at Q4_K_M is a 4.4 GB download (Hugging Face file listing for bartowski/Mistral-7B-Instruct-v0.3-GGUF), and the RTX 4060 has 8 GB of VRAM, leaving about 3.6 GB for the KV cache and runtime. Published runs put generation at a median of 50.9 tokens per second across 1 cited measurement. Fastest cited run: DatabaseMart.
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Published runs for Mistral 7B on the RTX 4060
| Generation | Quant | Runtime | Context | Source | Date |
|---|---|---|---|---|---|
| 50.9 tok/s | Q4_K_M | ollama | — | DatabaseMartlabelled "mistral:7b" | 2025-02-01 |
More about this pair
How much VRAM does Mistral 7B need?
The Q4_K_M file is 4.4 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 5.9 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What is the fastest Mistral 7B can generate on the RTX 4060?
Generation reads every weight once per token, so the RTX 4060's 272 GB/s of memory bandwidth divided by the 4.4 GB file sets a ceiling near 62 tokens per second. Real runs land below it; SpecPicks rejects any published figure above it.
Check another pair
Mistral 7B on other GPUs
- RTX 3060 Ti — Runs, 71.2 tok/s median
- RTX 3060 12GB — Runs
- Intel Arc B580 — Runs, 14.4 tok/s median
- RTX 4070 SUPER — Runs
- RTX 4060 Ti 16GB — Runs
- RTX 5060 Ti 16GB — Runs, 90.2 tok/s median
- Intel Arc A770 16GB — Runs, 41.5 tok/s median
- Radeon RX 9070 XT — Runs
- RTX 4070 Ti SUPER — Runs
- RTX 4080 — Runs
- RTX 5070 Ti — Runs, 112 tok/s median
- RTX 5080 — Runs
- RTX 3090 — Runs
- Radeon RX 7900 XTX — Runs
- RTX 4090 — Runs
- RTX 5090 — Runs
- RTX A6000 48GB — Runs
- RTX PRO 6000 Blackwell 96GB — Runs
Other models on the RTX 4060
- Llama 3.2 1B — Runs
- Llama 3.2 3B — Runs
- Qwen2.5 7B — Runs, 42.2 tok/s median
- Llama 3.1 8B — Runs, 41.7 tok/s median
- DeepSeek-R1-Distill-Llama 8B — Runs, 41.3 tok/s median
- Qwen3 8B — Runs
- Gemma 2 9B — Runs, 18 tok/s median
- Gemma 3 12B — Tight fit
- Qwen2.5 14B — Offload
- DeepSeek-R1-Distill-Qwen 14B — Offload
- Qwen3 14B — Offload
- Phi-4 14B — Offload
- gpt-oss-20b — Offload
- Gemma 3 27B — Offload
- Gemma 2 27B — Offload
- Qwen3 30B-A3B — Offload
- Qwen3 32B — Offload
- Qwen2.5 32B — Offload
- DeepSeek-R1-Distill-Qwen 32B — Offload
- Llama 3.1 70B — Offload
- Llama 3.3 70B — Offload
- DeepSeek-R1-Distill-Llama 70B — Offload
How the checks work
The fit is the Q4_K_M file size against the card's VRAM, with 1.5 GB kept for the runtime and an ~8K context. A published run counts only if the pair fits, the run is Q4-class, its source does not call it an estimate, it is under the bandwidth ceiling above, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.
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This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.