RTX 5060 Ti 16GBOpção econômica16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX 4070 Super 12GB?
YES — With Offload
C50Usable
Estimated from fit model
GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~12.3 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~33 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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, 34.1 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
34.1 tok/s
TTFT
5680 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 4070 Super 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: 34.1 tok/s decode · 5.7s TTFT (warm) · 85 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 | 47.7 tok/s | 2213 ms | 13K |
| Coding | C | Runs with offload | 32.5 tok/s | 5964 ms | 13K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 26.2 tok/s | 10757 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 34.1 tok/s | 6712 ms | 13K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 26.2 tok/s | 13446 ms | 13K |
Quantization options
How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX 4070 Super 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 startOpções de upgrade
Hardware que roda bem GGUF SOLARized GraniStral 14B 1902 YeAM HCT
RTX 5060 Ti 16GBOpção econômicaRTX 4060 Ti 16GBMelhor custo-benefício16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.25.8 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 2000 Ada 16GBUpgrade NVIDIA16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.25.6 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$625 MSRP
Frequently asked questions
Can RTX 4070 Super 12GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?
Yes, RTX 4070 Super 12GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs with offload). Expected decode speed: 32.5 tok/s.
How much VRAM does GGUF SOLARized GraniStral 14B 1902 YeAM HCT need?
GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B parameters) requires approximately 12.3 GB of memory with Q4_K_M quantization.
What is the best quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?
The recommended quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT is Q4_K_M, which balances quality and memory efficiency.
What speed will GGUF SOLARized GraniStral 14B 1902 YeAM HCT run at on RTX 4070 Super 12GB?
On RTX 4070 Super 12GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 32.5 tokens per second decode speed with a time-to-first-token of 5964ms using Q4_K_M quantization.
Can RTX 4070 Super 12GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?
For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 4070 Super 12GB receives a C grade with 32.5 tok/s and 13K context.
What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RTX 4070 Super 12GB?
On RTX 4070 Super 12GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 13K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if GGUF SOLARized GraniStral 14B 1902 YeAM HCT feels slow on RTX 4070 Super 12GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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