Llama 3.3 70B Instruct needs ~65.2 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~4 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
3.8 tok/s
TTFT
50468 ms
Safe context
101K
Memory
65.2 GB / 108.8 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 3.8 tok/s | 27528 ms | 101K |
| Coding | C | Runs well | 3.8 tok/s | 50468 ms | 101K |
| Agentic Coding | C | Runs well | 3.8 tok/s | 73409 ms | 101K |
| Reasoning | C | Runs well | 3.8 tok/s | 59644 ms | 101K |
| RAG | C | Runs well | 3.8 tok/s | 91761 ms | 101K |
How Llama 3.3 70B Instruct (70B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C43 |
Q3_K_S | 3 | 34.3 GB | Low | C44 |
NVFP4 | 4 | 39.2 GB | Medium | C45 |
Q4_K_M | 4 | 42.7 GB | Medium | C46 |
Q5_K_M | 5 | 50.4 GB | High | C48 |
Q6_K | 6 | 57.4 GB | High | C48 |
Q8_0Best for your GPU | 8 | 74.9 GB | Very High | C48 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Copy-paste commands to run Llama 3.3 70B Instruct on your machine.
Run
lms load hf-maziyarpanahi--llama-3-3-70b-instruct-gguf && lms server startOpciones de mejora
NVIDIA H200 141GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 2384%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 2384%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA B200 180GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 4042%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Llama 3.3 70B Instruct with a C grade (Runs well). Expected decode speed: 3.8 tok/s.
Llama 3.3 70B Instruct (70B parameters) requires approximately 65.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Llama 3.3 70B Instruct achieves approximately 3.8 tokens per second decode speed with a time-to-first-token of 50468ms using Q4_K_M quantization.
For coding workloads, Llama 3.3 70B Instruct on NVIDIA DGX Spark 128GB receives a C grade with 3.8 tok/s and 101K context.
On NVIDIA DGX Spark 128GB, Llama 3.3 70B Instruct can safely use up to 101K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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.
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