Radeon Pro W7900 48GBBudget pick
864 GB/s (+464)
A
Raises estimated decode speed by about 90%.98 tok/s decode
Raises estimated decode speed by about 90%.
~$3,999 MSRP
InternLM 7B needs ~19.9 GB VRAM. MacBook Pro M1 Max 64GB has 46.1 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
8K
Memory
19.9 GB / 46.1 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 | 51.5 tok/s | 2050 ms | 8K |
| Coding | B | Runs well | 51.5 tok/s | 3758 ms | 8K |
| Agentic Coding | A | Runs well | 51.5 tok/s | 5466 ms | 8K |
| Reasoning | B | Runs well | 51.5 tok/s | 4441 ms | 8K |
| RAG | A | Runs well | 51.5 tok/s | 6832 ms | 8K |
How InternLM 7B (7B params) fits at each quantization level on MacBook Pro M1 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B62 |
Q3_K_S | 3 | 3.4 GB | Low | B62 |
NVFP4 | 4 |
Copy-paste commands to run InternLM 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "InternLM/InternLM-7B" \
--hf-file "InternLM-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Raises estimated decode speed by about 90%.
~$3,999 MSRP
Raises estimated decode speed by about 90%.
~$3,999 MSRP
Yes, MacBook Pro M1 Max 64GB can run InternLM 7B with a B grade (Runs well). Expected decode speed: 51.5 tok/s.
InternLM 7B (7B parameters) requires approximately 19.9 GB of memory with Q4_K_M quantization.
The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Max 64GB, InternLM 7B 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, InternLM 7B on MacBook Pro M1 Max 64GB receives a B grade with 51.5 tok/s and 8K context.
On MacBook Pro M1 Max 64GB, InternLM 7B 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/internlm-7b-on-m1-max-64gb" 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 |
| B62 |
Q4_K_M | 4 | 4.3 GB | Medium | B62 |
Q5_K_M | 5 | 5.0 GB | High | B62 |
Q6_K | 6 | 5.7 GB | High | B63 |
Q8_0 | 8 | 7.5 GB | Very High | B63 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B65 |
Not always. MacBook Pro M1 Max 64GB 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.