How much VRAM does Nemotron 4 340B Instruct need?
Nemotron 4 340B Instruct needs about 202.3 GB of VRAM at Q4_K_M, or 354.0 GB at Q8_0 and 665.7 GB at FP16. No single GPU in our set holds it even at Q4_K_M; it needs multi-GPU or offload. 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 Nemotron 4 340B Instruct?
Pick your card — the answer below is computed by the same engine that produces every figure on this page.
H200 141GB cannot hold Nemotron 4 340B Instruct at Q4_K_M: it needs 202.3 GB against 133.9 GB usable; a heavier quantization, a larger card, or splitting across GPUs are the ways forward.
Weights plus runtime overhead. KV cache is not included — it depends on context length and this model’s published architecture.
| Weights (Q4_K_M) | 192.0 GB |
| Runtime overhead | 10.3 GB |
| Estimated total | 202.3 GB |
| Usable VRAM (95% of 141 GB) | 133.9 GB |
Across every GPU we track
- RTX 3060 12GBNot enough-190.9 GB
- RTX 4060 Ti 16GBNot enough-187.1 GB
- RTX 4070 Ti SUPERNot enough-187.1 GB
- RTX 3090Not enough-179.5 GB
- RTX 4090Not enough-179.5 GB
- RTX 5090Not enough-171.9 GB
- A100 40GBNot enough-164.3 GB
- RTX A6000Not enough-156.7 GB
- L40SNot enough-156.7 GB
- A100 80GBNot enough-126.3 GB
- H100 80GBNot enough-126.3 GB
- H200 141GBNot enough-68.4 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 | 633.3 GB | 665.7 GB |
| Q8_0 | 336.4 GB | 354.0 GB |
| Q6_K | 259.7 GB | 273.4 GB |
| Q5_K_M | 225.2 GB | 237.2 GB |
| Q4_K_M | 192.0 GB | 202.3 GB |
| Q4_0 | 178.1 GB | 187.8 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?
Nemotron 4 340B Instruct is a multi-GPU or datacenter model: even Q4_K_M needs about 202.3 GB, beyond any single card. Plan for several 80 GB GPUs or a hosted endpoint; FP16 (665.7 GB) is server-only.
Single-GPU fit at Q4_K_M (202.3 GB + KV)
RTX 3060 12GBRTX 4060 Ti 16GBRTX 4070 Ti SUPERRTX 3090RTX 4090RTX 5090A100 40GBRTX A6000L40SA100 80GBH100 80GBH200 141GB
Common questions
How much VRAM does Nemotron 4 340B Instruct need?
Nemotron 4 340B Instruct needs about 202.3 GB of VRAM at Q4_K_M, 354.0 GB at Q8_0, or 665.7 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 Nemotron 4 340B Instruct?
No single GPU in our set fits it at Q4_K_M; it needs multiple GPUs or CPU offload. At FP16 no single GPU in our set is sufficient.
Can Nemotron 4 340B Instruct run on 24 GB?
Not at Q4_K_M: it needs about 202.3 GB, more than the ~22.8 GB usable on a 24 GB card. Use a larger card, multiple GPUs, or offload.
More in the Nemotron family
Nemotron Nano 9B v2 InstructNemotron Super 49B InstructNemotron Llama-3.1 51B InstructNemotron Llama-3.1 70B InstructNemotron Ultra 253B Instruct