RX 7900 XT 20GBコスパ重視
20 GB VRAM (+4)800 GB/s (+512)
C
Raises estimated decode speed by about 187%.56.2 tok/sデコード
Raises estimated decode speed by about 187%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RX 7600 XT 16GB?
YES — Runs Great
C51Usable
Estimated from fit model
GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~12.7 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~20 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 12.7 GB, 19.6 tok/s, Runs well
12.7 GB required16.0 GB available
79% VRAM used
Fit status
Runs well
Decode
19.6 tok/s
TTFT
9898 ms
Safe context
48K
Memory
12.7 GB / 16.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on RX 7600 XT 16GB
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: 19.6 tok/s decode · 9.9s TTFT (warm) · 49 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 19.6 tok/s | 5399 ms | 48K |
| Coding | C | Runs well | 19.6 tok/s | 9898 ms | 48K |
| Agentic Coding | C | Tight fit | 19.6 tok/s | 14397 ms | 48K |
| Reasoning | C | Runs well | 19.6 tok/s | 11698 ms | 48K |
| RAG | C | Tight fit | 19.6 tok/s | 17996 ms | 48K |
Inference speed
GGUF SOLARized GraniStral 14B 1902 YeAM HCT inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for GGUF SOLARized GraniStral 14B 1902 YeAM HCT 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 1902 YeAM HCT (14B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C49 |
Q3_K_S | 3 | 6.9 GB | Low | C50 |
NVFP4 | 4 | 7.8 GB | Medium | C51 |
Q4_K_M | 4 | 8.5 GB | Medium | C51 |
Q5_K_M | 5 | 10.1 GB | High | C51 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | C50 |
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 startアップグレードオプション
GGUF SOLARized GraniStral 14B 1902 YeAM HCTを快適に動かすハードウェア
Frequently asked questions
Can RX 7600 XT 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?
Yes, RX 7600 XT 16GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 19.6 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 12.7 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 RX 7600 XT 16GB?
On RX 7600 XT 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 19.6 tokens per second decode speed with a time-to-first-token of 9898ms using Q4_K_M quantization.
Can RX 7600 XT 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?
For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RX 7600 XT 16GB receives a C grade with 19.6 tok/s and 48K context.
What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RX 7600 XT 16GB?
On RX 7600 XT 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 48K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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