Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$6,500 MSRP
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VOOZH | about |
Command R+ 104B needs ~49.7 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q2_K quantization, expect ~13 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
24.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.2 tok/s
TTFT
45648 ms
Safe context
4K
Memory
72.6 GB / 48.0 GB
Offload
30%
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.5 tok/s | 23681 ms | 4K |
| Coding | F | Too heavy | 4.2 tok/s | 45648 ms | 4K |
| Agentic Coding | F | Too heavy | 3.8 tok/s | 73153 ms | 4K |
| Reasoning | F | Too heavy | 4.2 tok/s | 53948 ms | 4K |
| RAG | F | Too heavy | 3.8 tok/s | 91441 ms | 4K |
How Command R+ 104B (104B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 40.6 GB | Low | F0 |
Q3_K_S | 3 | 51.0 GB | Low | F0 |
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 options
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$6,500 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$9,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$9,999 MSRP