Command R+ 104B needs ~78.1 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~9 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
9.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~7.3 GB host RAM)
Decode
8.5 tok/s
TTFT
22865 ms
Safe context
4K
Memory
78.1 GB / 69.1 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 7.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Very compromised (needs ~6.1 GB host RAM) | 8.7 tok/s | 12106 ms | 4K |
| Coding | C | Very compromised (needs ~7.3 GB host RAM) | 8.5 tok/s | 22865 ms | 4K |
| Agentic Coding | C | Very compromised (needs ~9.7 GB host RAM) | 8.0 tok/s | 35155 ms | 4K |
| Reasoning | C | Very compromised (needs ~7.3 GB host RAM) | 8.5 tok/s | 27022 ms | 4K |
| RAG | C | Very compromised (needs ~9.7 GB host RAM) | 8.0 tok/s | 43944 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Command R+ 104B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 10.3 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 9.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 8.0 | Tight |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 7.5 | Tight |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 6.3 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 5.5 | Too big |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 4.4 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 3.8 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 3.3 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.9 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.2 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 2.0 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Command R+ 104B (104B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 40.6 GB | Low | B65 |
Q3_K_SBest for your GPU | 3 | 51.0 GB | Low | B65 |
NVFP4 | 4 | 58.2 GB | Medium | F0 |
Q4_K_M | 4 | 63.4 GB | Medium | F0 |
Q5_K_M | 5 | 74.9 GB | High | F0 |
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-plusUpgrade-Optionen
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
NVIDIA GH200 96GBGrößter SprungRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 553%.
ca. $30,000 MSRP
Yes, MacBook Pro M4 Max 96GB can run Command R+ 104B with a C grade (Very compromised (needs ~7.3 GB host RAM)). Expected decode speed: 8.5 tok/s.
Command R+ 104B (104B parameters) requires approximately 78.1 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 M4 Max 96GB, Command R+ 104B achieves approximately 8.5 tokens per second decode speed with a time-to-first-token of 22865ms using Q4_K_M quantization.
For coding workloads, Command R+ 104B on MacBook Pro M4 Max 96GB receives a C grade with 8.5 tok/s and 4K context.
On MacBook Pro M4 Max 96GB, Command R+ 104B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M4 Max 96GB 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-m4-max-96gb" 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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