Meta Llama 3.1 8B Instruct needs ~20.1 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~34 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
33.6 tok/s
TTFT
5768 ms
Safe context
1.5M
Memory
20.1 GB / 108.8 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 33.6 tok/s | 3146 ms | 1.5M |
| Coding | C | Runs well | 33.6 tok/s | 5768 ms | 1.5M |
| Agentic Coding | C | Runs well | 33.6 tok/s | 8390 ms | 1.5M |
| Reasoning | C | Runs well | 33.6 tok/s | 6817 ms | 1.5M |
| RAG | C | Runs well | 33.6 tok/s | 10487 ms | 1.5M |
How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | D40 |
Q3_K_S | 3 | 3.9 GB | Low | D40 |
NVFP4 | 4 |
Copy-paste commands to run Meta Llama 3.1 8B Instruct on your machine.
Run
lms load hf-bartowski--meta-llama-3-1-8b-instruct-gguf && lms server startUpgrade options
NVIDIA H200 141GBBudget pickRaises estimated decode speed by about 233%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBBest valueRaises estimated decode speed by about 233%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA upgradeRaises estimated decode speed by about 233%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Meta Llama 3.1 8B Instruct with a C grade (Runs well). Expected decode speed: 33.6 tok/s.
Meta Llama 3.1 8B Instruct (8B parameters) requires approximately 20.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Meta Llama 3.1 8B Instruct is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Meta Llama 3.1 8B Instruct achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5768ms using Q4_K_M quantization.
For coding workloads, Meta Llama 3.1 8B Instruct on NVIDIA DGX Spark 128GB receives a C grade with 33.6 tok/s and 1.5M context.
On NVIDIA DGX Spark 128GB, Meta Llama 3.1 8B Instruct can safely use up to 1.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--meta-llama-3-1-8b-instruct-gguf-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| D40 |
Q4_K_M | 4 | 4.9 GB | Medium | D40 |
Q5_K_M | 5 | 5.8 GB | High | D40 |
Q6_K | 6 | 6.6 GB | High | D40 |
Q8_0 | 8 | 8.6 GB | Very High | D40 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C41 |
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.