VOOZH about

URL: https://willitrunai.com/can-run/hf-unsloth--falcon-h1-1-5b-instruct-gguf-on-gtx-1080-8gb

⇱ Falcon H1 1.5B Instruct on GTX 1080 8GB? YES

Can Falcon H1 1.5B Instruct run on GTX 1080 8GB?

YES — Runs Great

C45Usable
○Estimated from fit model

Falcon H1 1.5B Instruct needs ~3.1 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 3.1 GB, 21.0 tok/s, Runs well
3.1 GB required8.0 GB available
39% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

463K

Memory

3.1 GB / 8.0 GB

Memory breakdown

Weights0.9 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsFalcon H1 1.5B Instruct on GTX 1080 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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well21.0 tok/s5029 ms407K
CodingCRuns well21.0 tok/s9219 ms463K
Agentic CodingCRuns well21.0 tok/s13410 ms463K
ReasoningCRuns well21.0 tok/s10895 ms463K
RAGCRuns well21.0 tok/s16762 ms463K

Quantization options

How Falcon H1 1.5B Instruct (1.5B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.6 GB
LowC49
Q3_K_S
3
0.7 GB
LowC49
NVFP4
4
0.8 GB
MediumC49
Q4_K_M
4
0.9 GB
MediumC49
Q5_K_M
5
1.1 GB
HighC50
Q6_K
6
1.2 GB
HighC50
Q8_0
8
1.6 GB
Very HighC51
F16Best for your GPU
16
3.1 GB
MaximumC53

Get started

Copy-paste commands to run Falcon H1 1.5B Instruct on your machine.

Run

lms load hf-unsloth--falcon-h1-1-5b-instruct-gguf && lms server start

Frequently asked questions

See all results for GTX 1080 8GBSee all hardware for Falcon H1 1.5B Instruct