Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on RX 7600 XT 16GB?

YES — Runs Great

C52Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV 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.

Capabilities:

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 2102 YeAM HCT 32QKV 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

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (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 2102 YeAM HCT 32QKV on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv && lms server start

Opções de upgrade

Hardware que roda bem GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV

RX 7900 XT 20GBOpção econômica

20 GB VRAM (+4)800 GB/s (+512)

Raises estimated decode speed by about 187%.56.2 tok/s decodificação

Raises estimated decode speed by about 187%.

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

RX 7900 XTX 24GBMelhor custo-benefício

24 GB VRAM (+8)960 GB/s (+672)

Raises estimated decode speed by about 313%.80.9 tok/s decodificação

Raises estimated decode speed by about 313%.

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, RX 7600 XT 16GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 19.6 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 12.7 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 RX 7600 XT 16GB?

On RX 7600 XT 16GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV 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 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV 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 2102 YeAM HCT 32QKV use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 48K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GB See all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV

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