Raises estimated decode speed by about 2359%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored needs ~49.2 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~6 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
5.6 tok/s
TTFT
34607 ms
Safe context
186K
Memory
49.2 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 | 5.6 tok/s | 18876 ms | 186K |
| Coding | C | Runs well | 5.6 tok/s | 34607 ms | 186K |
| Agentic Coding | C | Runs well | 5.6 tok/s | 50337 ms | 186K |
| Reasoning | C | Runs well | 5.6 tok/s | 40899 ms | 186K |
| RAG | C | Runs well | 5.6 tok/s | 62922 ms | 186K |
How Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored (48B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.7 GB | Low | C41 |
Q3_K_S | 3 | 23.5 GB | Low | C41 |
NVFP4 | 4 | 26.9 GB | Medium | C42 |
Q4_K_M | 4 | 29.3 GB | Medium | C43 |
Q5_K_M | 5 | 34.6 GB | High | C44 |
Q6_K | 6 | 39.4 GB | High | C45 |
Q8_0Best for your GPU | 8 | 51.4 GB | Very High | C48 |
F16 | 16 | 98.4 GB | Maximum | F0 |
Copy-paste commands to run Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored on your machine.
Run
lms load hf-davidau--qwen3-48b-a4b-savant-commander-distill-12x-closed-open-heretic-uncensored-gguf && lms server startOpções de upgrade
Raises estimated decode speed by about 2359%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Raises estimated decode speed by about 2359%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Raises estimated decode speed by about 3998%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored with a C grade (Runs well). Expected decode speed: 5.6 tok/s.
Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored (48B parameters) requires approximately 49.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored achieves approximately 5.6 tokens per second decode speed with a time-to-first-token of 34607ms using Q4_K_M quantization.
For coding workloads, Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored on NVIDIA DGX Spark 128GB receives a C grade with 5.6 tok/s and 186K context.
On NVIDIA DGX Spark 128GB, Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored can safely use up to 186K 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.
Paste this snippet into any page to show a live fit card.
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