Model × hardware check
Can Gemma 4 31B run on NVIDIA DGX Spark · 128 GB?
Yes, this is a comfortable estimate at 8,192 tokens. a workstation-class dense model with a much larger memory footprint DGX Spark provides 128 GB of unified system memory and 273 GB/s of bandwidth in a compact CUDA-capable machine.
Short answer
Comfortable fit
At least 20% memory headroom remains after weights, KV cache, and runtime overhead.
- Available shared CPU/GPU memory
- 128 GB
- Estimated requirement
- 36.5 GB
- Headroom
- +91.5 GB
- Fit ratio
- 351%
- Recommended quantization
- Q8_0
- Context ceiling
- 262,144 tokens
- Speed estimate
- Estimate · Slow relative band
What the numbers say
The selected Gemma 4 31B package uses 33.8 GB of weight files. The 8K KV cache estimate is 1.2 GB, and the formula adds 1.5 GB of runtime overhead. That produces a 36.5 GB requirement against 128 GB of shared CPU/GPU memory.
This compact AI system uses unified system memory for CPU and GPU work. It is not a conventional add-in GPU, and runtime support must be checked for the exact software stack.
The context ceiling shown above is the point where the same formula reaches the machine's capacity. It is not a promise that the backend can allocate that full context after the operating system and other applications take their share.
Quantization trade-offs at 8K
| Quantization | Weight files | 8K requirement | Fit on this machine | Context ceiling | Speed estimate | Ollama |
|---|---|---|---|---|---|---|
| Q4_K_M | 19.52 GB | 22.2 GB | Comfortable fit · 577% | 262,144 tokens | Estimate · Slow relative band | ollama run gemma4:31b-it-q4_K_M |
| Q8_0 | 33.84 GB | 36.5 GB | Comfortable fit · 351% | 262,144 tokens | Estimate · Slow relative band | ollama run gemma4:31b-it-q8_0 |
Run commands
ollama run gemma4:31b-it-q8_0lms get "Gemma 4 31B"@q8_0
lms load <model-key-from-lms-ls> --gpu max --context-length 8192llama-cli -hf google/gemma-4-31B-it -c 8192 -ngl 99 -p "Your prompt"mlx_lm.generate --model google/gemma-4-31B-it --prompt "Your prompt" --max-tokens 8192Commands are examples, not a guarantee that the selected quantization tag is available in every runtime. Confirm the package name and license on the linked model page.
If it does not fit
This pairing already clears the comfort threshold
The current estimate leaves +91.5 GB. A longer context or another GPU application can reduce that margin, so verify free memory before relying on it.
Low-cost setup upgrades
Useful upgrades around NVIDIA DGX Spark · 128 GB
These are optional setup improvements, not a claim that an accessory can increase GPU or unified-memory capacity.
External NVMe SSD for model storage
Keeps large GGUF and MLX files off the boot drive. Fast storage reduces load time, but it does not increase the memory available for inference.
Search current listings on AmazonDesktop RAM upgrade kit
Useful for MoE CPU offload and keeping the operating system out of VRAM. Match the motherboard's memory type, slot layout, capacity limit, and validated part numbers.
Search current listings on AmazonGPU support bracket
A lower-cost mechanical upgrade for heavy three-slot cards. Check clearance, mounting height, and whether the bracket blocks fans or case panels.
Search current listings on AmazonAs an Amazon Associate I earn from qualifying purchases. Links are sponsored search links and do not change the price you pay. Affiliate disclosure.
Where this estimate can break
- A Q4 build plus KV cache is a workstation-class workload, not a typical 16 GB laptop fit.
- The GGUF package needs the multimodal projector for image input.
- Other GPU applications, display buffers, and the operating system reduce usable memory.
- Longer prompts, concurrent requests, and a different backend can raise peak memory.
Compare this pairing
FAQ
Questions about this pairing.
How much memory does Gemma 4 31B need on NVIDIA DGX Spark · 128 GB?
At an 8K context with Q8_0, the estimate is 36.5 GB: 33.8 GB of weights, 1.2 GB of KV cache, and 1.5 GB of runtime overhead. The available shared CPU/GPU memory is 128 GB.
What context length can NVIDIA DGX Spark · 128 GB handle with this model?
The calculated ceiling is 262,144 tokens for this quantization, capped by the model's native 262,144-token context. Actual usable context can be lower after the OS, other applications, and backend workspace.
How does quantization change the memory requirement on NVIDIA DGX Spark · 128 GB?
At 8,192 tokens, the published variants calculate as follows: Q4_K_M: 22.2 GB, comfortable fit; Q8_0: 36.5 GB, comfortable fit. A lower-bit file can move a result from too large to tight, but quality and kernel support may differ.
What can I run if Gemma 4 31B does not fit on NVIDIA DGX Spark · 128 GB?
This selection already clears the comfort threshold. If you later increase the context, monitor the ceiling of 262,144 tokens; smaller alternatives include Flint 40B, NVIDIA Nemotron 3.5 Bolt E4B, LFM3-1.7B.
Is the speed estimate for Gemma 4 31B on NVIDIA DGX Spark · 128 GB measured?
No. Slow relative band is a heuristic band derived from 30.7B active parameters and 273 GB/s published memory bandwidth. Backend kernels, batch size, thermals, and CPU offload can change actual tokens per second.