RX 7900 XTX 24GBBest value
960 GB/s (+560)
C
Raises estimated decode speed by about 90%.98 tok/s decode
Raises estimated decode speed by about 90%.
~$999 MSRP
Mistral 7B Instruct v0.2 needs ~9.4 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~52 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
51.5 tok/s
TTFT
3758 ms
Safe context
281K
Memory
9.4 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 | C | Runs well | 51.5 tok/s | 2050 ms | 281K |
| Coding | C | Runs well | 51.5 tok/s | 3758 ms | 281K |
| Agentic Coding | C | Runs well | 51.5 tok/s | 5466 ms | 281K |
| Reasoning | C | Runs well | 51.5 tok/s | 4441 ms | 281K |
| RAG | C | Runs well | 51.5 tok/s | 6832 ms | 281K |
How Mistral 7B Instruct v0.2 (7B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C45 |
Q3_K_S | 3 | 3.4 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run Mistral 7B Instruct v0.2 on your machine.
Run
lms load hf-thebloke--mistral-7b-instruct-v0-2-gguf && lms server startUpgrade options
Raises estimated decode speed by about 90%.
~$999 MSRP
Raises estimated decode speed by about 28%.
~$2,499 MSRP
Yes, MacBook Pro M1 Max 32GB can run Mistral 7B Instruct v0.2 with a C grade (Runs well). Expected decode speed: 51.5 tok/s.
Mistral 7B Instruct v0.2 (7B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral 7B Instruct v0.2 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Max 32GB, Mistral 7B Instruct v0.2 achieves approximately 51.5 tokens per second decode speed with a time-to-first-token of 3758ms using Q4_K_M quantization.
For coding workloads, Mistral 7B Instruct v0.2 on MacBook Pro M1 Max 32GB receives a C grade with 51.5 tok/s and 281K context.
On MacBook Pro M1 Max 32GB, Mistral 7B Instruct v0.2 can safely use up to 281K tokens of context. The model's official context limit is —, 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/hf-thebloke--mistral-7b-instruct-v0-2-gguf-on-m1-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 |
| C45 |
Q4_K_M | 4 | 4.3 GB | Medium | C46 |
Q5_K_M | 5 | 5.0 GB | High | C46 |
Q6_K | 6 | 5.7 GB | High | C46 |
Q8_0 | 8 | 7.5 GB | Very High | C48 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C50 |
Not always. MacBook Pro M1 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.