RX 9060 XT 16GBBudget pick
16 GB VRAM (+8)320 GB/s (+32)
B
Adds memory headroom for longer context windows and future model growth.50.8 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Mistral 7B Instruct v0.3 needs ~7.9 GB VRAM. RX 7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~42 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 with offload
Decode
42.1 tok/s
TTFT
4604 ms
Safe context
8K
Memory
7.9 GB / 8.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 42.1 tok/s | 2511 ms | 8K |
| Coding | B | Runs with offload | 42.1 tok/s | 4604 ms | 8K |
| Agentic Coding | F | Too heavy | 20.2 tok/s | 13912 ms | 8K |
| Reasoning | B | Runs with offload | 42.1 tok/s | 5441 ms | 8K |
| RAG | F | Too heavy | 20.2 tok/s | 17390 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 RX 7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B65 |
Q3_K_S | 3 | 3.4 GB | Low | B66 |
NVFP4 | 4 | 3.9 GB | Medium | B66 |
Q4_K_M | 4 | 4.3 GB | Medium | B65 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | B65 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
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 startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Raises estimated decode speed by about 55%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Yes, RX 7600 8GB can run Mistral 7B Instruct v0.3 with a B grade (Runs with offload). Expected decode speed: 42.1 tok/s.
Mistral 7B Instruct v0.3 (7B parameters) requires approximately 7.9 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 RX 7600 8GB, Mistral 7B Instruct v0.3 achieves approximately 42.1 tokens per second decode speed with a time-to-first-token of 4604ms using Q4_K_M quantization.
For coding workloads, Mistral 7B Instruct v0.3 on RX 7600 8GB receives a B grade with 42.1 tok/s and 8K context.
On RX 7600 8GB, 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mistral-7b-instruct-v0.3-on-rx-7600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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