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
Can InternLM 7B run on MacBook Pro M1 Max 64GB?
YES — Runs Great
B70Good
○Estimated from fit model
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.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 19.9 GB, 51.5 tok/s, Runs well
19.9 GB required46.1 GB available
43% VRAM used
Fit status
Runs well
Decode
51.5 tok/s
TTFT
3758 ms
Safe context
8K
Memory
19.9 GB / 46.1 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom6.9 GB
See how fast it feels
See how fast it feelsInternLM 7B on MacBook Pro M1 Max 64GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 51.5 tok/s decode · 3.8s TTFT (warm) · 129 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| 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 |
Quantization options
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 | 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 |
Get started
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