Best local LLMs for NVIDIA DGX Spark 64GB
NVIDIA DGX Spark 64GB. Memory-fit math with KV cache, speeds labelled by how they were obtained, and commands you can paste.
Best for Coding · 16k context
| Model | Quant | Fit | Memory | Speed (generation) |
|---|---|---|---|---|
| Qwen3-Coder 30B-A3B | Q8_0 | fits fully | 34.1 GB | ~61 tok/s measured src |
| Laguna XS 2.1 | Q8_0 | fits fully | 36.3 GB | ~49 tok/s scaled from measured src |
| Nemotron 3 Nano 30B-A3B | Q8_0 | fits fully | 33.7 GB | ~58 tok/s scaled from measured src |
| Qwen3.6 35B-A3B | Q8_0 | fits fully | 37.3 GB | ~49 tok/s measured src |
| Ornith 1.5 35B-A3B | Q8_0 | fits fully | 38.2 GB | ~49 tok/s scaled from measured src |
llama-server -hf unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q8_0
Context length is an Ollama server setting: quit the Ollama app or stop the service first, or set it in its environment (launchctl on Mac, systemctl on Linux).
Best for Chat / general · 16k context
| Model | Quant | Fit | Memory | Speed (generation) |
|---|---|---|---|---|
| Gemma 4 12B | Q6_K | fits fully | 10.4 GB | ~17 tok/s scaled from measured src |
| Nemotron 3 Nano 30B-A3B | Q8_0 | fits fully | 33.7 GB | ~58 tok/s scaled from measured src |
| Qwen3.6 35B-A3B | Q8_0 | fits fully | 37.3 GB | ~49 tok/s measured src |
| Ornith 1.5 35B-A3B | Q8_0 | fits fully | 38.2 GB | ~49 tok/s scaled from measured src |
| Qwen3-Coder 30B-A3B | Q8_0 | fits fully | 34.1 GB | ~61 tok/s measured src |
llama-server -hf unsloth/gemma-4-12b-it-GGUF:Q6_K -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/gemma-4-12b-it-GGUF:Q6_K
Context length is an Ollama server setting: quit the Ollama app or stop the service first, or set it in its environment (launchctl on Mac, systemctl on Linux).
Best for Vision · 16k context
| Model | Quant | Fit | Memory | Speed (generation) |
|---|---|---|---|---|
| Gemma 4 12B | Q6_K | fits fully | 10.4 GB | ~17 tok/s scaled from measured src |
| Qwen3.6 35B-A3B | Q8_0 | fits fully | 37.3 GB | ~49 tok/s measured src |
| Gemma 4 26B-A4B | Q8_0 | fits fully | 27.4 GB | ~54 tok/s scaled from measured src |
| Qwen3.5 9B | Q8_0 | fits fully | 10.1 GB | ~17 tok/s scaled from measured src |
| Qwen3-VL 8B | Q8_0 | fits fully | 11.1 GB | ~19 tok/s scaled from measured src |
llama-server -hf unsloth/gemma-4-12b-it-GGUF:Q6_K -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/gemma-4-12b-it-GGUF:Q6_K
Context length is an Ollama server setting: quit the Ollama app or stop the service first, or set it in its environment (launchctl on Mac, systemctl on Linux).
All 27 models that fit, fastest first · 16k context
Click a column header to sort. 21 of 27 speeds here are measured or scaled from measured; the rest are bandwidth estimates. Filter by use case, speed source, fit, license and more in the interactive advisor →
| Model | Params | Quant | Fit | Memory | Tok/s | Source |
|---|---|---|---|---|---|---|
| LFM3 1.7B | 1.73B | Q8_0 | fits fully | 2.1 GB | ~110 | scaled from measured |
| gpt-oss 20B | 20.91B MoE | Q8_0 | fits fully | 12.5 GB | ~86 | scaled from measured |
| LFM2.5 24B-A2B | 23.97B MoE | Q8_0 | fits fully | 25.8 GB | ~66 | scaled from measured |
| Flint 40B-A2.5B | 39.79B MoE | Q8_0 | fits fully | 42.8 GB | ~64 | scaled from measured |
| Qwen3-Coder 30B-A3B | 30.53B MoE | Q8_0 | fits fully | 34.1 GB | ~61 | measured |
| Nemotron 3 Nano 30B-A3B | 31.58B MoE | Q8_0 | fits fully | 33.7 GB | ~58 | scaled from measured |
| Gemma 4 26B-A4B | 25.81B MoE | Q8_0 | fits fully | 27.4 GB | ~54 | scaled from measured |
| Nemotron 3 Nano 4B | 3.97B | Q8_0 | fits fully | 4.6 GB | ~53 | scaled from measured |
| Qwen3.5 4B | 4.66B | Q8_0 | fits fully | 5.1 GB | ~50 | scaled from measured |
| GLM-4.7-Flash | 31.22B MoE | Q8_0 | fits fully | 32.7 GB | ~50 | scaled from measured |
| Ornith 1.5 35B-A3B | 35.95B MoE | Q8_0 | fits fully | 38.2 GB | ~49 | scaled from measured |
| Qwen3.6 35B-A3B | 35.95B MoE | Q8_0 | fits fully | 37.3 GB | ~49 | measured |
| Laguna XS 2.1 | 33.44B MoE | Q8_0 | fits fully | 36.3 GB | ~49 | scaled from measured |
| Nemotron 3.5 Lightning 30B-A3B | 31.58B MoE | Q8_0 | fits fully | 35.2 GB | ~47 | scaled from measured |
| DeepSeek R1 Distill Qwen 7B | 7.62B | Q8_0 | fits fully | 9.0 GB | ~29 | scaled from measured |
| Gemma 4 E4B | 8B | Q8_0 | fits fully | 8.5 GB | ~29 | scaled from measured |
| Qwen3-VL 8B | 8.77B | Q8_0 | fits fully | 11.1 GB | ~19 | scaled from measured |
| Qwen3.5 9B | 9.65B | Q8_0 | fits fully | 10.1 GB | ~17 | scaled from measured |
| Ornith 1.5 9B | 9.65B | Q8_0 | fits fully | 10.1 GB | ~17 | scaled from measured |
| Gemma 4 12B | 11.96B | Q6_K | fits fully | 10.4 GB | ~17 | scaled from measured |
| Qwen3.8 27B | 27.78B | UD-Q4_K_M | fits fully | 17.7 GB | ~13 | estimated |
| Qwen3.6 27B | 27.78B | Q4_K_M | fits fully | 18.1 GB | ~13 | estimated |
| Gemma 4 31B | 31.27B | Q4_K_M | fits fully | 20.5 GB | ~12 | estimated |
| Mistral Small 3.2 24B | 24.01B | Q4_K_M | fits fully | 17.0 GB | ~11 | scaled from measured |
| DeepSeek R1 Distill Qwen 32B | 32.76B | Q4_K_M | fits fully | 24.1 GB | ~11 | estimated |
| Nanbeige4.3 33B | 33.12B | Q4_K_M | fits fully | 24.3 GB | ~11 | estimated |
| Llama 3.3 70B | 70.55B | Q4_K_M | fits fully | 47.9 GB | ~5 | estimated |
Long context on this hardware · up to 1M tokens
Largest context whose KV cache still fits next to the weights (same placement as the 16k result, KV types f16 / q8_0 / q4_0). Models are capped at their verified maximum; extended means beyond the native maximum using YaRN, only listed where the model card documents it. Long context needs flash attention (-fa on, already in the commands) and a lot of memory; a quantized KV cache saves memory but can cost accuracy. Speed at depth is an estimate (penalty fitted to measured depth runs up to 128k).
Try it in the advisor: 128k · 256k · 512k · 1M
| Model | Native max | Max ctx f16 | Max ctx q8_0 | Max ctx q4_0 | Est. tok/s at max f16 ctx |
|---|---|---|---|---|---|
| Qwen3.5 4B Q8_0 | 256k | 1M extended | 1M extended | 1M extended | ~8 estimated |
| Qwen3.5 9B Q8_0 | 256k | 1M extended | 1M extended | 1M extended | ~5 estimated |
| Qwen3.6 35B-A3B Q8_0 | 256k | 512k extended | 1M extended | 1M extended | ~14 estimated |
| Qwen3.8 27B UD-Q4_K_M | 256k | 512k extended | 1M extended | 1M extended | ~5 estimated |
| Qwen3.6 27B Q4_K_M | 256k | 512k extended | 1M extended | 1M extended | ~5 estimated |
| LFM3 1.7B Q8_0 | 256k | 256k | 256k | 256k | ~48 estimated |
| Flint 40B-A2.5B Q8_0 | 256k | 256k | 256k | 256k | ~21 estimated |
| Nemotron 3 Nano 30B-A3B Q8_0 | 256k | 256k | 256k | 256k | ~44 estimated |
| Gemma 4 26B-A4B Q8_0 | 256k | 256k | 256k | 256k | ~27 estimated |
| Nemotron 3 Nano 4B Q8_0 | 256k | 256k | 256k | 256k | ~30 estimated |
| Ornith 1.5 35B-A3B Q8_0 | 256k | 256k | 256k | 256k | ~22 estimated |
| Laguna XS 2.1 Q8_0 | 256k | 256k | 256k | 256k | ~14 estimated |
| Nemotron 3.5 Lightning 30B-A3B Q8_0 | 256k | 256k | 256k | 256k | ~34 estimated |
| Qwen3-VL 8B Q8_0 | 256k | 256k | 512k extended | 1M extended | ~4 estimated |
| Ornith 1.5 9B Q8_0 | 256k | 256k | 256k | 256k | ~10 estimated |
| Gemma 4 12B Q6_K | 256k | 256k | 256k | 256k | ~12 estimated |
| Gemma 4 31B Q4_K_M | 256k | 256k | 256k | 256k | ~6 estimated |
| gpt-oss 20B Q8_0 | 128k | 128k | 128k | 128k | ~40 estimated |
| LFM2.5 24B-A2B Q8_0 | 128k | 128k | 128k | 128k | ~37 estimated |
| Qwen3-Coder 30B-A3B Q8_0 | 256k | 128k | 256k | 512k extended | ~16 estimated |
| GLM-4.7-Flash Q8_0 | 198k | 128k | 128k | 128k | ~18 estimated |
| DeepSeek R1 Distill Qwen 7B Q8_0 | 128k | 128k | 128k | 128k | ~17 estimated |
| Gemma 4 E4B Q8_0 | 128k | 128k | 128k | 128k | ~24 estimated |
| Mistral Small 3.2 24B Q4_K_M | 128k | 128k | 128k | 128k | ~5 estimated |
| Nanbeige4.3 33B Q4_K_M | 512k | 128k | 256k | 512k | ~5 estimated |
| DeepSeek R1 Distill Qwen 32B Q4_K_M | 128k | 64k | 128k | 128k | ~7 estimated |
| Llama 3.3 70B Q4_K_M | 128k | 32k | 32k | 64k | ~4 estimated |
FAQ
What is the best local LLM for NVIDIA DGX Spark 64GB?
Of the 27 models that fit fully at 16k context, LFM3 1.7B Q8_0 is the fastest at ~110 tok/s (scaled from measured); the largest is Llama 3.3 70B Q4_K_M.
How much memory can NVIDIA DGX Spark 64GB use for a model?
About 53.9 GB usable for the model and KV cache, with 273 GB/s memory bandwidth.
Can NVIDIA DGX Spark 64GB run long contexts?
See the long-context table above: it lists the largest context whose KV cache fits next to the weights for every model, up to 1M tokens where the model supports it.
Open the interactive advisor with this hardware →
Speeds marked measured are medians of real runs (short-context generation) from these sources; scaled from measured means a measurement of a same-size or same-model-different-quant run, scaled by bytes read; estimated comes from memory bandwidth (spec source: https://www.nvidia.com/en-us/products/workstations/dgx-spark/) × an efficiency factor of 0.81 calibrated to measured runs on this hardware. 19 measured data points on file for this hardware · submit your benchmark. Memory size on this page is an example configuration; check what your machine has. Model data last verified 2026-11-02. Ranking = fits and fast enough first, then larger; not a quality benchmark.
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