Mistral 7B Instruct v0.3 needs ~8.6 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
97.7 tok/s
TTFT
1982 ms
Safe context
8K
Memory
8.6 GB / 12.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 97.7 tok/s | 1081 ms | 8K |
| Coding | B | Runs well | 97.7 tok/s | 1982 ms | 8K |
| Agentic Coding | B | Tight fit | 97.7 tok/s | 2882 ms | 8K |
| Reasoning | B | Runs well | 97.7 tok/s | 2342 ms | 8K |
| RAG | B | Tight fit | 97.7 tok/s | 3603 ms | 8K |
Inference speed
Estimated decode speed (tokens/sec) for Mistral 7B Instruct v0.3 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 95.2 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 60.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 59.8 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 55.4 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 48.7 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.0 | Offloads |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B61 |
Q3_K_S | 3 | 3.4 GB | Low | B62 |
NVFP4 | 4 | 3.9 GB | Medium | B63 |
Q4_K_M | 4 | 4.3 GB | Medium | B63 |
Q5_K_M | 5 | 5.0 GB | High | B64 |
Q6_K | 6 | 5.7 GB | High | B65 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B64 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Copy-paste commands to run Mistral 7B Instruct v0.3 on your machine.
Run
lms load Mistral-7B-Instruct-v0.3 && lms server startYes, RTX 4070 Super 12GB can run Mistral 7B Instruct v0.3 with a B grade (Runs well). Expected decode speed: 97.7 tok/s.
Mistral 7B Instruct v0.3 (7B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral 7B Instruct v0.3 is Q4_K_M, which balances quality and memory efficiency.
On RTX 4070 Super 12GB, Mistral 7B Instruct v0.3 achieves approximately 97.7 tokens per second decode speed with a time-to-first-token of 1982ms using Q4_K_M quantization.
For coding workloads, Mistral 7B Instruct v0.3 on RTX 4070 Super 12GB receives a B grade with 97.7 tok/s and 8K context.
On RTX 4070 Super 12GB, Mistral 7B Instruct v0.3 can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mistral-7b-instruct-v0.3-on-rtx-4070-super-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: