RTX 5060 Ti 16GBコスパ重視16 GB VRAM (+4)448 GB/s (+160)
C
Raises estimated decode speed by about 68%.31.5 tok/sデコード
Raises estimated decode speed by about 68%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on RTX A2000 12GB?
YES — With Offload
C48Usable
Estimated from fit model
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~12.3 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~19 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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, 18.8 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
18.8 tok/s
TTFT
10303 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 2102 YeAM HCT 32QKV on RTX A2000 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: 18.8 tok/s decode · 10.3s TTFT (warm) · 47 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 | 26.3 tok/s | 4015 ms | 13K |
| Coding | C | Runs with offload | 18.8 tok/s | 10303 ms | 13K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 14.4 tok/s | 19512 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 18.8 tok/s | 12176 ms | 13K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 14.4 tok/s | 24390 ms | 13K |
Quantization options
How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on RTX A2000 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 2102 YeAM HCT 32QKV on your machine.
Run
lms load hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv && lms server startアップグレードオプション
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKVを快適に動かすハードウェア
RTX 5060 Ti 16GBコスパ重視RTX 4060 Ti 16GBコスパ最良16 GB VRAM (+4)
C
Raises estimated decode speed by about 37%.25.8 tok/sデコード
Raises estimated decode speed by about 37%.
Adds memory headroom for longer context windows and future model growth.
〜$499 MSRP
RTX 2000 Ada 16GBNVIDIAアップグレード16 GB VRAM (+4)
C
Raises estimated decode speed by about 36%.25.6 tok/sデコード
Raises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
〜$625 MSRP
Frequently asked questions
Can RTX A2000 12GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?
Yes, RTX A2000 12GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs with offload). Expected decode speed: 18.8 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 12.3 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 RTX A2000 12GB?
On RTX A2000 12GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 18.8 tokens per second decode speed with a time-to-first-token of 10303ms using Q4_K_M quantization.
Can RTX A2000 12GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?
For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on RTX A2000 12GB receives a C grade with 18.8 tok/s and 13K context.
What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on RTX A2000 12GB?
On RTX A2000 12GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV 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 2102 YeAM HCT 32QKV feels slow on RTX A2000 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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