RX 7900 XTX 24GBBudget pick
960 GB/s (+560)
B
Raises estimated decode speed by about 68%.98 tok/s decode
Raises estimated decode speed by about 68%.
~$999 MSRP
Mistral 7B Instruct v0.3 needs ~10.6 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~58 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
58.4 tok/s
TTFT
3315 ms
Safe context
8K
Memory
10.6 GB / 23.0 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 58.4 tok/s | 1808 ms | 8K |
| Coding | B | Runs well | 58.4 tok/s | 3315 ms | 8K |
| Agentic Coding | B | Runs well | 58.4 tok/s | 4821 ms | 8K |
| Reasoning | B | Runs well | 58.4 tok/s | 3917 ms | 8K |
| RAG | B | Runs well | 58.4 tok/s | 6027 ms | 8K |
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B57 |
Q3_K_S | 3 | 3.4 GB | Low | B57 |
NVFP4 | 4 |
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
Raises estimated decode speed by about 68%.
~$999 MSRP
RTX 4090 24GBBest valueRaises estimated decode speed by about 92%.
~$1,599 MSRP
Yes, MacBook Pro M2 Max 32GB can run Mistral 7B Instruct v0.3 with a B grade (Runs well). Expected decode speed: 58.4 tok/s.
Mistral 7B Instruct v0.3 (7B parameters) requires approximately 10.6 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 MacBook Pro M2 Max 32GB, Mistral 7B Instruct v0.3 achieves approximately 58.4 tokens per second decode speed with a time-to-first-token of 3315ms using Q4_K_M quantization.
For coding workloads, Mistral 7B Instruct v0.3 on MacBook Pro M2 Max 32GB receives a B grade with 58.4 tok/s and 8K context.
On MacBook Pro M2 Max 32GB, 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-m2-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B57 |
Q4_K_M | 4 | 4.3 GB | Medium | B58 |
Q5_K_M | 5 | 5.0 GB | High | B58 |
Q6_K | 6 | 5.7 GB | High | B58 |
Q8_0 | 8 | 7.5 GB | Very High | B60 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B62 |
Not always. MacBook Pro M2 Max 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.