How much VRAM does Command R+ 104B (08-2024) Chat need?
Command R+ 104B (08-2024) Chat needs about 62.4 GB of VRAM at Q4_K_M, or 108.8 GB at Q8_0 and 204.2 GB at FP16. No consumer card holds it at Q4_K_M. The smallest fit in our set is the A100 80GB (80 GB). Weights plus runtime overhead. Budget separately for KV cache, which at 128K is a material share.
Weights-plus-runtime footprint across the six most common quantizations. KV cache is context-dependent and comes on top. At 128K it is gigabytes on its own, before the weights; size it precisely in the GPU Memory Calculator.
Can your GPU run Command R+ 104B (08-2024) Chat?
Pick your card — the answer below is computed by the same engine that produces every figure on this page.
A100 80GB runs Command R+ 104B (08-2024) Chat at Q4_K_M with 13.6 GB to spare; comfortable at this size, where most cards cannot hold the weights at all.
Weights plus runtime overhead. KV cache is not included — it depends on context length and this model’s published architecture.
| Weights (Q4_K_M) | 58.7 GB |
| Runtime overhead | 3.7 GB |
| Estimated total | 62.4 GB |
| Usable VRAM (95% of 80 GB) | 76.0 GB |
Across every GPU we track
- RTX 3060 12GBNot enough-51.0 GB
- RTX 4060 Ti 16GBNot enough-47.2 GB
- RTX 4070 Ti SUPERNot enough-47.2 GB
- RTX 3090Not enough-39.6 GB
- RTX 4090Not enough-39.6 GB
- RTX 5090Not enough-32.0 GB
- A100 40GBNot enough-24.4 GB
- RTX A6000Not enough-16.8 GB
- L40SNot enough-16.8 GB
- A100 80GBRecommended+13.6 GB
- H100 80GBRecommended+13.6 GB
- H200 141GBExcellent+71.5 GB
Estimates, not guarantees: real usage moves with runtime, driver, batch size and context. Size a specific context window in the GPU Memory Calculator.
How was this number calculated? Every figure here comes from Bitpute’s documented calculation methodology — parameters, precision, quantization, runtime overhead and usable VRAM, each formula written out in full.
Memory by quantization
| Quant | Weights | + runtime overhead |
|---|---|---|
| FP16 | 193.7 GB | 204.2 GB |
| Q8_0 | 102.9 GB | 108.8 GB |
| Q6_K | 79.4 GB | 84.1 GB |
| Q5_K_M | 68.9 GB | 73.1 GB |
| Q4_K_M | 58.7 GB | 62.4 GB |
| Q4_0 | 54.5 GB | 58.0 GB |
Overhead = 0.75 GB + 5% of weights (CUDA context, buffers). Add KV cache on top. Weights dominate at this size, but a 128K window still costs gigabytes.
Which quantization should you actually run?
Command R+ 104B (08-2024) Chat is too large for a 12–16 GB card. On A100 80GB, Q4_K_M (62.4 GB) fits with plenty of headroom for long context — the realistic single-GPU entry point. Higher quality (Q6_K 84.1 GB, FP16 204.2 GB) needs H200 141GB or multiple GPUs — for occasional use, renting is usually cheaper than buying.
Single-GPU fit at Q4_K_M (62.4 GB + KV)
RTX 3060 12GBRTX 4060 Ti 16GBRTX 4070 Ti SUPERRTX 3090RTX 4090RTX 5090A100 40GBRTX A6000L40SA100 80GBH100 80GBH200 141GB
Where the memory goes
Weights and overhead are exact for Q4_K_M. The KV bar is a generic 32-layer transformer at 8K; at this scale the weights dominate, but long context still adds gigabytes — size it in the calculator is for.
Deciding how much headroom to buy at this tier? A100 80GB vs H200 141GB compares them on bandwidth, power and which models each one holds.
Common questions
How much VRAM does Command R+ 104B (08-2024) Chat need?
Command R+ 104B (08-2024) Chat needs about 62.4 GB of VRAM at Q4_K_M, 108.8 GB at Q8_0, or 204.2 GB at FP16. That is weights plus about 0.75 GB of runtime overhead and 5% of weight size; KV cache is additional and depends on context length.
What GPU can run Command R+ 104B (08-2024) Chat?
At Q4_K_M the smallest card in our set that fits is the A100 80GB (80 GB usable at 95%). At FP16 no single GPU in our set is sufficient.
Can Command R+ 104B (08-2024) Chat run on 24 GB?
Not at Q4_K_M: it needs about 62.4 GB, more than the ~22.8 GB usable on a 24 GB card. Use a larger card, multiple GPUs, or offload.
Same memory footprint as 1 other checkpoint
Command R+ 104B (08-2024) Chat has an identical parameter count to this, so every figure on this page applies to it unchanged. They are separate releases at the same size, so the memory budget is identical — check each model card for capability differences.