Command R+ 104B needs ~81.6 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~4 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
Tight fit
Decode
4.1 tok/s
TTFT
47057 ms
Safe context
66K
Memory
81.6 GB / 92.2 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 4.1 tok/s | 25668 ms | 66K |
| Coding | B | Tight fit | 4.1 tok/s | 47057 ms | 66K |
| Agentic Coding | B | Tight fit | 4.1 tok/s | 68447 ms | 66K |
| Reasoning | B | Tight fit | 4.1 tok/s | 55613 ms | 66K |
| RAG | B | Tight fit | 4.1 tok/s | 85559 ms | 66K |
How Command R+ 104B (104B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 40.6 GB | Low | B63 |
Q3_K_S | 3 | 51.0 GB | Low | B65 |
NVFP4 | 4 | 58.2 GB | Medium | B65 |
Q4_K_M | 4 | 63.4 GB | Medium | B65 |
Q5_K_MBest for your GPU | 5 | 74.9 GB | High | B65 |
Q6_K | 6 | 85.3 GB | High | F0 |
Q8_0 | 8 | 111.3 GB | Very High | F0 |
F16 | 16 | 213.2 GB | Maximum | F0 |
Copy-paste commands to run Command R+ 104B on your machine.
Run
ollama run command-r-plusOpções de upgrade
Raises estimated decode speed by about 132%.
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Raises estimated decode speed by about 2341%.
Adds memory headroom for longer context windows and future model growth.
~$8,000 MSRP
NVIDIA GH200 96GBMaior saltoRaises estimated decode speed by about 1254%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$30,000 MSRP
Yes, MacBook Pro M3 Max 128GB can run Command R+ 104B with a B grade (Tight fit). Expected decode speed: 4.1 tok/s.
Command R+ 104B (104B parameters) requires approximately 81.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Command R+ 104B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 128GB, Command R+ 104B achieves approximately 4.1 tokens per second decode speed with a time-to-first-token of 47057ms using Q4_K_M quantization.
For coding workloads, Command R+ 104B on MacBook Pro M3 Max 128GB receives a B grade with 4.1 tok/s and 66K context.
On MacBook Pro M3 Max 128GB, Command R+ 104B can safely use up to 66K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Pro M3 Max 128GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/command-r-plus-104b-on-m3-max-128gb" 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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