Command R 35B VRAM Calculator
Estimate GPU memory to run Command R 35B — weights, KV cache and overhead at FP16/INT8/INT4, with a fits-on-which-GPU verdict.
Cohere's Command R 35B is tuned for RAG and tool use at 128K context; with 8 KV heads on a wide 8192 hidden size, cache planning dominates deployment decisions.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About Command R 35B VRAM Calculator
This calculator estimates how much GPU memory (VRAM) you need to run Command R 35B locally or in production. It sums the three real costs of inference: the model weights at your chosen precision (FP16, INT8 or INT4), the key-value attention cache that grows with context length and concurrent sequences, and ~10% runtime overhead for CUDA buffers and fragmentation. Cohere's Command R 35B is tuned for RAG and tool use at 128K context; with 8 KV heads on a wide 8192 hidden size, cache planning dominates deployment decisions. Use the precision and context sliders to find the cheapest GPU that actually fits your workload instead of guessing from the parameter count alone.
How to use Command R 35B VRAM Calculator
- 1Enter your values into Command R 35B VRAM Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Command R 35B VRAM Calculator?
- ✓Computes Command R 35B VRAM instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: VRAM ≈ 1.1 × (P×bytes(precision) + 2×layers×kv_heads×head_dim×ctx×batch×bytes(kv)) — Command R 35B: P=35B, layers=40, .
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
What makes Command R's memory profile RAG-friendly?+
RAG stuffs long retrieved passages into the prompt, so cache cost per token matters more than weight size. With GQA (8 KV heads × 128 head_dim), a 64K-token RAG context costs ~5.2 GB at FP16 — manageable next to 70 GB of FP16 weights.
Single-GPU options for Command R 35B?+
FP16 weights (~70 GB) need an 80 GB card; INT8 (~35 GB) fits a 48 GB A6000/L40S with cache headroom; 4-bit (~18 GB) runs on a 24 GB card for short-to-medium contexts. Long-context RAG pushes you to 48 GB+.
How accurate is this Command R 35B VRAM estimate?+
It uses the exact architecture from the model's config.json (layers, heads, KV heads, head dimension) and standard serving math, so weight and cache figures are typically within a few percent. Real usage varies with your inference engine's allocator, paged-attention block size and activation buffers.
Does quantizing the KV cache hurt quality?+
INT8/FP8 KV cache is widely used in production (vLLM, TensorRT-LLM) with negligible quality loss on most tasks, and it halves cache memory. It matters most for long contexts, where the cache rivals or exceeds the weight memory.
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