GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~13.8 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~77 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
76.7 tok/s
TTFT
2523 ms
Safe context
116K
Memory
13.8 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 76.7 tok/s | 1376 ms | 116K |
| Coding | C | Runs well | 76.7 tok/s | 2523 ms | 116K |
| Agentic Coding | C | Runs well | 76.7 tok/s | 3670 ms | 116K |
| Reasoning | C | Runs well | 76.7 tok/s | 2982 ms | 116K |
| RAG | C | Runs well | 76.7 tok/s | 4588 ms | 116K |
Inference speed
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.
How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C45 |
Q3_K_S | 3 | 6.9 GB | Low | C46 |
NVFP4 | 4 | 7.8 GB | Medium | C47 |
Q4_K_M | 4 | 8.5 GB | Medium | C47 |
Q5_K_M | 5 | 10.1 GB | High | C48 |
Q6_K | 6 | 11.5 GB | High | C49 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | C50 |
F16 | 16 | 28.7 GB | Maximum | F0 |
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 startYes, RTX 3090 24GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 76.7 tok/s.
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B parameters) requires approximately 13.8 GB of memory with Q4_K_M quantization.
The recommended quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV is Q4_K_M, which balances quality and memory efficiency.
On RTX 3090 24GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 76.7 tokens per second decode speed with a time-to-first-token of 2523ms using Q4_K_M quantization.
For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on RTX 3090 24GB receives a C grade with 76.7 tok/s and 116K context.
On RTX 3090 24GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 116K 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-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv-on-rtx-3090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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