RX 7600 XT 16GB经济之选
16 GB VRAM (+8)288 GB/s (+32)
C
Raises estimated decode speed by about 52%.35.1 tok/s 解码
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can EXAONE 3.5 7.8B Instruct i1 run on RX 590 8GB?
YES — Tight Fit
C49Usable
Estimated from fit model
EXAONE 3.5 7.8B Instruct i1 needs ~7.4 GB VRAM. RX 590 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 7.4 GB, 23.1 tok/s, Tight fit
7.4 GB required8.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
23.1 tok/s
TTFT
8368 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 i1 on RX 590 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: 23.1 tok/s decode · 8.4s TTFT (warm) · 58 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 23.1 tok/s | 4564 ms | 27K |
| Coding | C | Tight fit | 23.1 tok/s | 8368 ms | 27K |
| Agentic Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 15.6 tok/s | 18026 ms | 27K |
| Reasoning | C | Tight fit | 23.1 tok/s | 9889 ms | 27K |
| RAG | C | Runs with offload (needs ~0.2 GB host RAM) | 15.6 tok/s | 22532 ms | 27K |
Quantization options
How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C54 |
Q3_K_S | 3 | 3.8 GB | Low | C53 |
NVFP4 | 4 | 4.4 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.8 GB | Medium | C53 |
Q5_K_M | 5 | 5.6 GB | High | F0 |
Q6_K | 6 | 6.4 GB | High | F0 |
Q8_0 | 8 | 8.3 GB | Very High | F0 |
F16 | 16 | 16.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run EXAONE 3.5 7.8B Instruct i1 on your machine.
Run
lms load hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf && lms server start升级选项
能流畅运行 EXAONE 3.5 7.8B Instruct i1 的硬件
RX 9060 XT 16GB最佳性价比
16 GB VRAM (+8)320 GB/s (+64)
C
Raises estimated decode speed by about 84%.42.4 tok/s 解码
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
RX 7700 XT 12GBAMD 升级
12 GB VRAM (+4)432 GB/s (+176)
C
Raises estimated decode speed by about 136%.54.5 tok/s 解码
Raises estimated decode speed by about 136%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Frequently asked questions
Can RX 590 8GB run EXAONE 3.5 7.8B Instruct i1?
Yes, RX 590 8GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Tight fit). Expected decode speed: 23.1 tok/s.
How much VRAM does EXAONE 3.5 7.8B Instruct i1 need?
EXAONE 3.5 7.8B Instruct i1 (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 i1?
The recommended quantization for EXAONE 3.5 7.8B Instruct i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will EXAONE 3.5 7.8B Instruct i1 run at on RX 590 8GB?
On RX 590 8GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 23.1 tokens per second decode speed with a time-to-first-token of 8368ms using Q4_K_M quantization.
Can RX 590 8GB run EXAONE 3.5 7.8B Instruct i1 for coding?
For coding workloads, EXAONE 3.5 7.8B Instruct i1 on RX 590 8GB receives a C grade with 23.1 tok/s and 27K context.
What context window can EXAONE 3.5 7.8B Instruct i1 use on RX 590 8GB?
On RX 590 8GB, EXAONE 3.5 7.8B Instruct i1 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 i1 feels slow on RX 590 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf-on-rx-590-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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