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Can I run Command R 35B?

Command R 35B by Cohere needs around 32 GB of RAM at the recommended 4-bit quantization (21.2 GB download). Your hardware is checked below — instantly, nothing leaves your browser. Expect roughly ~16 tok/s on a Apple M-series Max.

Reading your hardware signals…

Real-world notes

Command R 35B is Cohere's RAG-and-chat workhorse, and it asks more of your hardware than the 7-8B models most people start with. At 4-bit it weighs about 21 GB, so the 12 GB RTX 3060 is out entirely, and you need at least 32 GB of system RAM to load it comfortably. The realistic home for it is a 24 GB card like an RTX 4090, or an Apple Silicon Mac with plenty of unified memory. Drop to a 2-bit quant (about 15 GB) only if you are squeezed and willing to accept the quality hit.

In daily use it is steady rather than snappy. On an RTX 4090 you can expect around 40 tokens per second at 4-bit, which streams faster than you read; on an M-series Max it settles near 16 tok/s, usable but noticeably more deliberate; and on a CPU with DDR5 you are looking at roughly 2 tok/s, which is patience-testing territory. The 128K context window is genuinely large, but it is expensive to use: fill it and total memory climbs to about 54 GB, so unless you have a 48 GB-plus setup, keep working context modest and reserve the full window for occasional long documents.

Against the current crop it is showing its 2024 age. The Qwen 3.5 and 3.6 35B-A3B models in the related list are MoE designs that generally feel quicker and stronger on reasoning and coding, where Command R was never the focus. Its standout trait remains what Cohere built it for: grounded retrieval-augmented chat with citations, where it tends to stay on-source rather than wander. One hard caveat, though: the weights are CC-BY-NC, so this is non-commercial only. You can run it for personal projects and research, but you cannot ship it in a product or any paid service.

Specifications

Parameters35B
Context window128K tokens
ProviderCohere
LicenseCC-BY-NC
Released2024-08
Best forChat

Size by quantization

QuantizationBits/weightDownloadMin RAMQuality
Q2_K3.3514.7 GB24 GBNoticeable loss
Q4_K_MRecommended4.8521.2 GB32 GBRecommended
Q5_K_M5.6524.7 GB48 GBHigh
Q8_08.537.2 GB48 GBNear-original
F161670.0 GB96 GBOriginal

Sizes are estimates from parameter count × bits per weight; real GGUF builds vary slightly. · Data updated: 2026-06-11 · How we calculate these numbers →

Memory needed by context length

ContextKV cache (est.)Total memory (Q4)
4K tokens~1.0 GB~22.2 GB
8K tokens~2.0 GB~23.2 GB
32K tokens~8.1 GB~29.3 GB
128K tokens~32.6 GB~53.8 GB

The KV cache grows with context length — a model that fits at 4K can run out of memory at 32K. Estimates assume an FP16 cache with grouped-query attention; actual usage varies by runtime.

Estimated speed by hardware

HardwareBandwidth~Speed
NVIDIA RTX 3060 12GB360 GB/sWon't fit in VRAM
NVIDIA RTX 4090 24GB1008 GB/s~40 tok/s
Apple M-series (base)100 GB/s~4 tok/s
Apple M-series Pro270 GB/s~11 tok/s
Apple M-series Max410 GB/s~16 tok/s
CPU only (dual-channel DDR5)60 GB/s~2 tok/s

Token generation is memory-bandwidth bound: tok/s ≈ bandwidth × 0.85 ÷ model size at Q4. Real-world numbers vary by runtime and context length.

Run it locally

The easiest path is Ollama — one command and you're chatting:

ollama run command-r

Frequently asked questions