Will It Run AI

Can EXAONE 3.5 7.8B Instruct run on RX 5700 XT 8GB?

YES — Tight Fit

C52Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~7.4 GB VRAM. RX 5700 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Share:

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) — 7.4 GB, 49.0 tok/s, Tight fit
7.4 GB required8.0 GB available
93% VRAM used

Fit status

Tight fit

Decode

49.0 tok/s

TTFT

3954 ms

Safe context

27K

Memory

7.4 GB / 8.0 GB

Memory breakdown

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

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on RX 5700 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: 49.0 tok/s decode · 4.0s TTFT (warm) · 122 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 fit49.0 tok/s2157 ms27K
CodingCTight fit49.0 tok/s3954 ms27K
Agentic CodingCRuns with offload34.1 tok/s8257 ms27K
ReasoningCTight fit49.0 tok/s4673 ms27K
RAGCRuns with offload34.1 tok/s10322 ms27K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC54
Q3_K_S
3
3.8 GB
LowC53
NVFP4
4
4.4 GB
MediumC53
Q4_K_MBest for your GPU
4
4.8 GB
MediumC53
Q5_K_M
5
5.6 GB
HighF0
Q6_K
6
6.4 GB
HighF0
Q8_0
8
8.3 GB
Very HighF0
F16
16
16.0 GB
MaximumF0

Get started

Copy-paste commands to run EXAONE 3.5 7.8B Instruct on your machine.

Run

lms load hf-lgai-exaone--exaone-3-5-7-8b-instruct-gguf && lms server start

升级选项

能流畅运行 EXAONE 3.5 7.8B Instruct 的硬件

RX 7700 XT 12GB经济之选
12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.54.5 tok/s 解码

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

~$449 MSRP

RX 6700 XT 12GB最佳性价比
12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s 解码

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

~$479 MSRP

RX 9070 16GBAMD 升级
16 GB VRAM (+8)640 GB/s (+192)
C
Raises estimated decode speed by about 70%.83.4 tok/s 解码

Raises estimated decode speed by about 70%.

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

~$479 MSRP

Frequently asked questions

Can RX 5700 XT 8GB run EXAONE 3.5 7.8B Instruct?

Yes, RX 5700 XT 8GB can run EXAONE 3.5 7.8B Instruct with a C grade (Tight fit). Expected decode speed: 49.0 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 3.5 7.8B Instruct?

The recommended quantization for EXAONE 3.5 7.8B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 7.8B Instruct run at on RX 5700 XT 8GB?

On RX 5700 XT 8GB, EXAONE 3.5 7.8B Instruct achieves approximately 49.0 tokens per second decode speed with a time-to-first-token of 3954ms using Q4_K_M quantization.

Can RX 5700 XT 8GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on RX 5700 XT 8GB receives a C grade with 49.0 tok/s and 27K context.

What context window can EXAONE 3.5 7.8B Instruct use on RX 5700 XT 8GB?

On RX 5700 XT 8GB, EXAONE 3.5 7.8B Instruct can safely use up to 27K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if EXAONE 3.5 7.8B Instruct feels slow on RX 5700 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 5700 XT 8GBSee all hardware for EXAONE 3.5 7.8B Instruct
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-lgai-exaone--exaone-3-5-7-8b-instruct-gguf-on-rx-5700-xt-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: