NVIDIA H200 141GB经济之选141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 921%.196 tok/s 解码
Raises estimated decode speed by about 921%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on NVIDIA DGX Spark 128GB?
YES — Runs Great
C41Usable
Estimated from fit model
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~24.4 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~19 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 24.4 GB, 19.2 tok/s, Runs well
24.4 GB required108.8 GB available
22% VRAM used
Fit status
Runs well
Decode
19.2 tok/s
TTFT
10094 ms
Safe context
839K
Memory
24.4 GB / 108.8 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 19.2 tok/s decode · 10.1s TTFT (warm) · 48 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 19.2 tok/s | 5506 ms | 839K |
| Coding | C | Runs well | 19.2 tok/s | 10094 ms | 839K |
| Agentic Coding | C | Runs well | 19.2 tok/s | 14682 ms | 839K |
| Reasoning | C | Runs well | 19.2 tok/s | 11929 ms | 839K |
| RAG | C | Runs well | 19.2 tok/s | 18352 ms | 839K |
Inference speed
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 140.6 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 89.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 80.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 76.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 75.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 65.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 54.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 51.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 35.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 35.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 33.2 | Offloads |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 28.1 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.7 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 19.5 | Offloads |
| RTX 4060 8GB | 8 GB | Q4_K_M | 7.2 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | D39 |
Q3_K_S | 3 | 6.9 GB | Low | D39 |
NVFP4 | 4 | 7.8 GB | Medium | D39 |
Q4_K_M | 4 | 8.5 GB | Medium | D39 |
Q5_K_M | 5 | 10.1 GB | High | D39 |
Q6_K | 6 | 11.5 GB | High | D39 |
Q8_0 | 8 | 15.0 GB | Very High | D40 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | C42 |
Get started
Copy-paste commands to run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on your machine.
Run
lms load hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv && lms server start升级选项
能流畅运行 GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV 的硬件
NVIDIA H200 141GB经济之选NVIDIA H200 PCIe 141GB最佳性价比141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 921%.196 tok/s 解码
Raises estimated decode speed by about 921%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA 升级180 GB VRAM (+52)8000 GB/s (+7727)
C
Raises estimated decode speed by about 921%.196 tok/s 解码
Raises estimated decode speed by about 921%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?
Yes, NVIDIA DGX Spark 128GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 19.2 tok/s.
How much VRAM does GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV need?
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B parameters) requires approximately 24.4 GB of memory with Q4_K_M quantization.
What is the best quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?
The recommended quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV is Q4_K_M, which balances quality and memory efficiency.
What speed will GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 19.2 tokens per second decode speed with a time-to-first-token of 10094ms using Q4_K_M quantization.
Can NVIDIA DGX Spark 128GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?
For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on NVIDIA DGX Spark 128GB receives a C grade with 19.2 tok/s and 839K context.
What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 839K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?
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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