Can NVIDIA DGX Spark (GB10) · 128 GB run Llama 3.3 70B Instruct?
128 GB unified · 273 GB/s memory bandwidth · Llama 3.3 70B Instruct has 70.6B parameters.
Short answer
Yes, acceptable for chat; borderline for coding questions.
Best quantization for chat: Q4_K_M (40 GB of weights) on Ollama. Generation: 4.6–6 tok/s, leaving about 76 GB free for your other apps.
How it rates for each workload
Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.
| Workload | Verdict | Quant | Generation | First reply | Context | |
|---|---|---|---|---|---|---|
| Chatwith Open WebUI | Acceptable | Q4_K_M | 4.6–6 tok/s | ≈3 s | 8K | Details |
| Coding questionswith Continue | Borderline | Q5_K_M | 3.8–5 tok/s | ≈15 s | 16K | Details |
| Coding in a repositorywith Aider | Borderline | Q5_K_M | 3.5–4.8 tok/s | ≈60 s | 32K | Details |
| Agentic codingwith OpenCode | Borderline | Q6_K | 3–4.1 tok/s | ≈85 s | 32K | Details |
| Long documents (64K)with Open WebUI | Borderline | Q4_K_M | 3.3–4.5 tok/s | ≈5 min | 64K | Details |
Speeds are estimates calibrated against measured benchmarks on similar hardware. “First reply” is the time to read the workload’s opening prompt. How we calculate
Get it running
Copy-paste commands for the Q4_K_M build with a 8K context. Ollama and llama.cpp download the file straight from Hugging Face.
ollama run hf.co/unsloth/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
Downloads 42.52 GB (Q4_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=8192 before `ollama serve` for long prompts.
Other models for NVIDIA DGX Spark (GB10) · 128 GB
The best-rated models for chat on this machine.
- gpt-oss-120b≈55 tok/sExcellent
- Recursive Flint (40B-A2.5B)100–140 tok/sExcellent
- Qwen3.6 35B-A3B60–80 tok/sExcellent
- Qwen3-Next 80B-A3B Instruct60–80 tok/sExcellent
- Gemma 4 26B-A4B45–60 tok/sExcellent
- Qwen3.5 35B-A3B60–80 tok/sExcellent
Cheapest machines for Llama 3.3 70B Instruct
Hardware rated comfortable or better for chat, lowest price first.
- Desktop PC · 2× GeForce RTX 3090 (48 GB) · 128 GB RAM$2,884 · 11–18 tok/sComfortable
- Mac Studio M5 Ultra 128 GB$5,499 · 10–16 tok/sComfortable
- Mac Studio M5 Ultra 256 GB$6,299 · 10–16 tok/sComfortable
- Mac Studio M5 Ultra 512 GB$7,099 · 10–16 tok/sComfortable
- Desktop PC · RTX PRO 6000 Blackwell · 128 GB RAM$17,284 · 17–25 tok/sExcellent
Same chip, other memory: 64 GB
Different tool, context size or workload?
These pages use typical settings. The full check models your exact setup.