willitrun·ai

Can EXAONE 3.5 7.8B Instruct run on GTX 1660 Super 6GB?

BARELY — Tight on Memory

D33Poor
Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~7.2 GB VRAM. GTX 1660 Super 6GB has 6.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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) — 7.2 GB, 19.1 tok/s, Very compromised (needs ~0.8 GB host RAM)
7.2 GB required6.0 GB available
120% VRAM needed

1.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.8 GB host RAM)

Decode

19.1 tok/s

TTFT

10112 ms

Safe context

4K

Memory

7.2 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.6 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on GTX 1660 Super 6GB
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: 19.1 tok/s decode · 10.1s TTFT (warm) · 48 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 20% 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.

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

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 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatDVery compromised (needs ~0.5 GB host RAM)22.1 tok/s4773 ms4K
CodingDVery compromised (needs ~0.8 GB host RAM)19.1 tok/s10112 ms4K
Agentic CodingFToo heavy14.7 tok/s19141 ms4K
ReasoningDVery compromised (needs ~0.8 GB host RAM)19.1 tok/s11950 ms4K
RAGFToo heavy14.7 tok/s23926 ms4K

Inference speed

EXAONE 3.5 7.8B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for EXAONE 3.5 7.8B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~109 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M109.2Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M109.2Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M109.2Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M109.2Fits
RX 7900 XTX 24GB
24 GBQ4_K_M109.2Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M109.2Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M97.5Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M92.5Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M79.4Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M78.8Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M78.8Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M50.4Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M49.9Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M46.2Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M41.7Offloads
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M40.6Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
3.0 GB
LowC54
Q3_K_S
3
3.8 GB
LowF0
NVFP4
4
4.4 GB
MediumF0
Q4_K_M
4
4.8 GB
MediumF0
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-lmstudio-community--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

NVIDIARTX 3050 8GB经济之选
8 GB VRAM (+2)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.26.1 tok/s 解码

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

Raises estimated decode speed by about 37%.

~$249 MSRP

NVIDIARTX 5060 8GB最佳性价比
8 GB VRAM (+2)448 GB/s (+112)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.57.4 tok/s 解码

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

Raises estimated decode speed by about 201%.

~$299 MSRP

NVIDIARTX 5050 8GBNVIDIA 升级
8 GB VRAM (+2)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.39.5 tok/s 解码

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

Raises estimated decode speed by about 107%.

~$299 MSRP

Frequently asked questions

Can GTX 1660 Super 6GB run EXAONE 3.5 7.8B Instruct?

Yes, GTX 1660 Super 6GB can run EXAONE 3.5 7.8B Instruct with a D grade (Very compromised (needs ~0.8 GB host RAM)). Expected decode speed: 19.1 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 7.2 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 GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, EXAONE 3.5 7.8B Instruct achieves approximately 19.1 tokens per second decode speed with a time-to-first-token of 10112ms using Q4_K_M quantization.

Can GTX 1660 Super 6GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on GTX 1660 Super 6GB receives a D grade with 19.1 tok/s and 4K context.

What context window can EXAONE 3.5 7.8B Instruct use on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, EXAONE 3.5 7.8B Instruct can safely use up to 4K 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 GTX 1660 Super 6GB?

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.

See all results for GTX 1660 Super 6GBSee all hardware for EXAONE 3.5 7.8B Instruct
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