Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RX 6650 XT 8GB?

Q: What should I upgrade first if GGUF SOLARized GraniStral 14B 1902 YeAM HCT feels slow on RX 6650 XT 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

D37Poor

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~8.8 GB VRAM. RX 6650 XT 8GB has 8.0 GB. With Q2_K quantization, expect ~14 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.

GGUF SOLARized GraniStral 14B 1902 YeAM HCT at Q4_K_M needs 11.9 GB — too much for RX 6650 XT 8GB (8.0 GB). Runs at Q2_K (8.8 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.9 GB, exceeds 8.0 GB available

11.9 GB required8.0 GB available

149% VRAM needed

3.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.5 tok/s

TTFT

35490 ms

Safe context

Memory

11.9 GB / 8.0 GB

Offload

30%

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on RX 6650 XT 8GB
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: 5.5 tok/s decode · 35.5s TTFT (warm) · 14 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.3 tok/s	16652 ms	4K
Coding	F	Too heavy	5.5 tok/s	35490 ms	4K
Agentic Coding	F	Too heavy	4.2 tok/s	67778 ms	4K
Reasoning	F	Too heavy	5.5 tok/s	41943 ms	4K
RAG	F	Too heavy	4.2 tok/s	84722 ms	4K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RX 6650 XT 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	F0
Q3_K_S	3	6.9 GB	Low	F0
NVFP4	4

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

Upgrade options

Hardware that runs GGUF SOLARized GraniStral 14B 1902 YeAM HCT well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+8)288 GB/s (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.19.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+8)320 GB/s (+40)

Makes the model fit on the accelerator instead of staying completely out of reach.23.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

12 GB VRAM (+4)432 GB/s (+152)

Makes the model fit on the accelerator instead of staying completely out of reach.21.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

Frequently asked questions

Can RX 6650 XT 8GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RX 6650 XT 8GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT at Q2_K quantization (Very compromised (needs ~0.5 GB host RAM)). The recommended Q4_K_M requires 11.9 GB which exceeds available memory, but at Q2_K it needs only 8.8 GB. Expected decode speed: 13.6 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 11.9 GB at Q4_K_M quantization. On RX 6650 XT 8GB, it fits at Q2_K using 8.8 GB.

What is the best quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

The recommended quantization is Q4_K_M, but on RX 6650 XT 8GB the best fitting quantization is Q2_K, which uses 8.8 GB.

What speed will GGUF SOLARized GraniStral 14B 1902 YeAM HCT run at on RX 6650 XT 8GB?

On RX 6650 XT 8GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 13.6 tokens per second decode speed with a time-to-first-token of 14193ms using Q2_K quantization.

Can RX 6650 XT 8GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RX 6650 XT 8GB receives a F grade with 5.5 tok/s and 4K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RX 6650 XT 8GB?

See all results for RX 6650 XT 8GB See all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT

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