Command R 35B needs ~27.1 GB VRAM. RTX 3090 Ti 24GB has 24.0 GB. With Q4_K_M quantization, expect ~20 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
3.1 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.4 GB host RAM)
Decode
13.1 tok/s
TTFT
14787 ms
Safe context
4K
Memory
27.1 GB / 24.0 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.
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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~1.5 GB host RAM) | 14.4 tok/s | 7319 ms | 4K |
| Coding | B | Very compromised | 19.5 tok/s | 9938 ms | 4K |
| Agentic Coding | F | Too heavy | 10.9 tok/s | 25792 ms | 4K |
| Reasoning | B | Very compromised (needs ~2.4 GB host RAM) | 13.1 tok/s | 17475 ms | 4K |
| RAG | F | Too heavy | 10.9 tok/s | 32240 ms |
How Command R 35B (35B params) fits at each quantization level on RTX 3090 Ti 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | A76 |
Q3_K_SBest for your GPU | 3 | 17.2 GB | Low | A76 |
Copy-paste commands to run Command R 35B on your machine.
Run
ollama run command-rUpgrade options
RTX 5090 32GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 202%.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 192%.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 79%.
~$4,000 MSRP
Yes, RTX 3090 Ti 24GB can run Command R 35B with a B grade (Very compromised). Expected decode speed: 19.5 tok/s.
Command R 35B (35B parameters) requires approximately 27.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Command R 35B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3090 Ti 24GB, Command R 35B achieves approximately 19.5 tokens per second decode speed with a time-to-first-token of 9938ms using Q4_K_M quantization.
For coding workloads, Command R 35B on RTX 3090 Ti 24GB receives a B grade with 19.5 tok/s and 4K context.
On RTX 3090 Ti 24GB, Command R 35B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/command-r-35b-on-rtx-3090-ti-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
19.6 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 21.3 GB | Medium | F0 |
Q5_K_M | 5 | 25.2 GB | High | F0 |
Q6_K | 6 | 28.7 GB | High | F0 |
Q8_0 | 8 | 37.5 GB | Very High | F0 |
F16 | 16 | 71.8 GB | Maximum | F0 |
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.