Can the DGX Spark (GB10) run OTel-2.0-LLM-31B-IT-QLoRA?

Yes. OTel-2.0-LLM-31B-IT-QLoRA fits the DGX Spark (GB10)'s 128 GB at Q4_K_M (about 19.8 GB needed), running at an estimated 8.7 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
128 GB
Needs at Q4
19.8 GB
Headroom
108.2 GB
Speed (Q4_K_M)
~8.7 tok/s

Every quantization

OTel-2.0-LLM-31B-IT-QLoRA (31.3B) on 128 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M18 GB19.8 GBFits easily~8.7 tok/s
Q5_K_M22 GB23.8 GBFits easily~7.1 tok/s
Q8_033 GB34.8 GBFits easily~4.8 tok/s
FP1663 GB64.8 GBFits easily~2.5 tok/s

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the DGX Spark (GB10)'s 273.2 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

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Same model, other hardware - and other models on this card

OTel-2.0-LLM-31B-IT-QLoRA on other hardware

Also runs on the DGX Spark (GB10)

Frequently asked

Quick answers

Can the NVIDIA DGX Spark (GB10) run OTel-2.0-LLM-31B-IT-QLoRA?

Yes. OTel-2.0-LLM-31B-IT-QLoRA fits the DGX Spark (GB10)'s 128 GB at Q4_K_M (about 19.8 GB needed), running at an estimated 8.7 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does OTel-2.0-LLM-31B-IT-QLoRA need?

About 18 GB of weights at Q4_K_M, 33 GB at Q8_0 and 63 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is OTel-2.0-LLM-31B-IT-QLoRA on the DGX Spark (GB10)?

Roughly 8.7 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the DGX Spark (GB10)'s 273.2 GB/s memory bandwidth.