Command R+ 104B needs ~49.7 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q2_K quantization, expect ~6 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
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
72.6 GB / 48.0 GB
Offload
30%
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 5.8 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
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.
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.
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 | 2.1 tok/s | 51312 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 114400 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How Command R+ 104B (104B params) fits at each quantization level on NVIDIA L20 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-Optionen
NVIDIA A16 64GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 120%.
ca. $6,500 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige WahlMakes 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.
ca. $9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBNVIDIA-UpgradeMakes 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.
ca. $9,999 MSRP
Yes, NVIDIA L20 48GB can run Command R+ 104B at Q2_K quantization (Runs with offload (needs ~1.4 GB host RAM)). The recommended Q4_K_M requires 72.6 GB which exceeds available memory, but at Q2_K it needs only 49.7 GB. Expected decode speed: 5.8 tok/s.
Command R+ 104B (104B parameters) requires approximately 72.6 GB at Q4_K_M quantization. On NVIDIA L20 48GB, it fits at Q2_K using 49.7 GB.
The recommended quantization is Q4_K_M, but on NVIDIA L20 48GB the best fitting quantization is Q2_K, which uses 49.7 GB.
On NVIDIA L20 48GB, Command R+ 104B achieves approximately 5.8 tokens per second decode speed with a time-to-first-token of 33516ms using Q2_K quantization.
For coding workloads, Command R+ 104B on NVIDIA L20 48GB receives a F grade with 2.0 tok/s and 4K context.
On NVIDIA L20 48GB, Command R+ 104B can safely use up to 8K tokens of context at Q2_K quantization. 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.
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