16 GB VRAM (+4)448 GB/s (+16)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX 4080 Laptop 12GB?
YES — With Offload
C50Usable
○Estimated from fit model
GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~12.3 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced
Share:
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) — 12.3 GB, 29.6 tok/s, Runs with offload (needs ~0.2 GB host RAM)
12.3 GB required12.0 GB available
103% VRAM needed
0.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
29.6 tok/s
TTFT
6541 ms
Safe context
13K
Memory
12.3 GB / 12.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 4080 Laptop 12GB
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: 29.6 tok/s decode · 6.5s TTFT (warm) · 74 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 41.4 tok/s | 2549 ms | 13K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 29.6 tok/s | 6541 ms | 13K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 22.7 tok/s | 12389 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 29.6 tok/s | 7731 ms | 13K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 22.7 tok/s | 15486 ms | 13K |
Quantization options
How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C52 |
Q3_K_S | 3 | 6.9 GB | Low | C52 |
NVFP4 | 4 | 7.8 GB | Medium | C51 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | C51 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run GGUF SOLARized GraniStral 14B 1902 YeAM HCT on your machine.
Run
lms load hf-srs6901--gguf-solarized-granistral-14b-1902-yeam-hct && lms server startUpgrade options
Hardware that runs GGUF SOLARized GraniStral 14B 1902 YeAM HCT well
16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.25.8 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
16 GB VRAM (+4)896 GB/s (+464)
B
Raises estimated decode speed by about 120%.65.1 tok/s decode
Raises estimated decode speed by about 120%.
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP