Builds / 3x Nvidia GB10 (unified, DGX Spark)
3x DGX Spark: GLM-5.3 EXL3 2.75-bit on TensorFold, 31.3 tok/s (Mia's AI Lab recipe)
- GPU(s)
- 3x Nvidia GB10 (unified, DGX Spark)
- VRAM / unified memory (GB)
- 384
- CPU
- Nvidia GB10 Grace (Arm) per node
Amazon · eBay (used) · Newegg - Motherboard
- not stated
- RAM
- 128 GB LPDDR5X unified per Spark (384 GB total)
- PSU
- not stated
- Other
- 3 NVIDIA DGX Sparks, multi-node
- Reported total cost
- not stated
- Power
- not stated
Measured speed (generation tokens/sec)
| Model | Quant | Engine | tok/s | Prompt processing (tok/s) | Details |
|---|---|---|---|---|---|
| GLM-5.3 | EXL3 2.75-bit + DSpark | TensorFold, 3 nodes, 1 stream | 31.3 | 591 @ 32k-token prompt est. TTFT @8k ≈ 13.9 s (calculated from measured prefill) | ⚡ spec-decode (DSpark)context: ~500k📏 Single-stream decode as published on Mia's AI Lab recipe card (Decode speed, one stream). The card lists 41.7 tok/s on code, one stream. |
Prompt processing = prefill speed (how fast the prompt is read) as reported by the source, with the prompt length it was measured at; blank = the source does not report it. est. TTFT @8k is calculated from the measured prefill speed (8,192 ÷ prefill tok/s), shown only when prefill was measured on a prompt of 512+ tokens. It is not a measurement and ignores load time and caching. measured TTFT is the source's own time-to-first-token. Why prefill matters for coding assistants.
Recipe and numbers by Mia's AI Lab (mia-ai.net), recipe last updated 2026-10-04; RigBase read the recipe card on 2026-10-27 and checked the decode figure against the recipe page. RigBase did not re-run it. Prefill is prompt-processing speed on a 32k prompt where listed.
https://mia-ai.net/models
https://github.com/MiaAI-Lab/GLM-5.3-EXL3-3x-DGX-Sparks-TensorFold
Blank fields = not stated in the source. We never estimate build data; GPU street prices above are separately sourced and dated.
Quick answers: Can 3x DGX Spark (GB10) run GLM-5.3?