Will It Run AI

Can internlm3 8b instruct abliterated i1 run on RX 6600 XT 8GB?

YES — Tight Fit

C49Usable
Estimated from fit model

internlm3 8b instruct abliterated i1 needs ~7.5 GB VRAM. RX 6600 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
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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) — 7.5 GB, 26.2 tok/s, Tight fit
7.5 GB required8.0 GB available
94% VRAM used

Fit status

Tight fit

Decode

26.2 tok/s

TTFT

7381 ms

Safe context

24K

Memory

7.5 GB / 8.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsinternlm3 8b instruct abliterated i1 on RX 6600 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: 26.2 tok/s decode · 7.4s TTFT (warm) · 66 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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit26.2 tok/s4026 ms24K
CodingCTight fit26.2 tok/s7381 ms24K
Agentic CodingDRuns with offload (needs ~0.3 GB host RAM)17.5 tok/s16082 ms24K
ReasoningCTight fit26.2 tok/s8723 ms24K
RAGDRuns with offload (needs ~0.3 GB host RAM)17.5 tok/s20103 ms24K

Quantization options

How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on RX 6600 XT 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC53
Q3_K_S
3
3.9 GB
LowC53
NVFP4
4
4.5 GB
MediumC53
Q4_K_MBest for your GPU
4
4.9 GB
MediumC52
Q5_K_M
5
5.8 GB
HighF0
Q6_K
6
6.6 GB
HighF0
Q8_0
8
8.6 GB
Very HighF0
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run internlm3 8b instruct abliterated i1 on your machine.

Run

lms load hf-mradermacher--internlm3-8b-instruct-abliterated-i1-gguf && lms server start

Opções de upgrade

Hardware que roda bem internlm3 8b instruct abliterated i1

RX 9060 XT 16GBOpção econômica
16 GB VRAM (+8)320 GB/s (+64)
C
Raises estimated decode speed by about 58%.41.3 tok/s decodificação

Raises estimated decode speed by about 58%.

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

~$349 MSRP

RX 7700 XT 12GBMelhor custo-benefício
12 GB VRAM (+4)432 GB/s (+176)
C
Raises estimated decode speed by about 103%.53.1 tok/s decodificação

Raises estimated decode speed by about 103%.

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

~$449 MSRP

RX 6700 XT 12GBUpgrade AMD
12 GB VRAM (+4)384 GB/s (+128)
C
Raises estimated decode speed by about 56%.40.9 tok/s decodificação

Raises estimated decode speed by about 56%.

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

~$479 MSRP

Frequently asked questions

Can RX 6600 XT 8GB run internlm3 8b instruct abliterated i1?

Yes, RX 6600 XT 8GB can run internlm3 8b instruct abliterated i1 with a C grade (Tight fit). Expected decode speed: 26.2 tok/s.

How much VRAM does internlm3 8b instruct abliterated i1 need?

internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm3 8b instruct abliterated i1?

The recommended quantization for internlm3 8b instruct abliterated i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm3 8b instruct abliterated i1 run at on RX 6600 XT 8GB?

On RX 6600 XT 8GB, internlm3 8b instruct abliterated i1 achieves approximately 26.2 tokens per second decode speed with a time-to-first-token of 7381ms using Q4_K_M quantization.

Can RX 6600 XT 8GB run internlm3 8b instruct abliterated i1 for coding?

For coding workloads, internlm3 8b instruct abliterated i1 on RX 6600 XT 8GB receives a C grade with 26.2 tok/s and 24K context.

What context window can internlm3 8b instruct abliterated i1 use on RX 6600 XT 8GB?

On RX 6600 XT 8GB, internlm3 8b instruct abliterated i1 can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm3 8b instruct abliterated i1 feels slow on RX 6600 XT 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RX 6600 XT 8GBSee all hardware for internlm3 8b instruct abliterated i1
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