Can Falcon H1 1.5B Instruct run on GTX 1070 Ti 8GB?

YES — Runs Great

C45Usable

Estimated from fit model

Falcon H1 1.5B Instruct needs ~3.1 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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.

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 1070 Ti 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	21.0 tok/s	5029 ms	407K
Coding	C	Runs well	21.0 tok/s	9219 ms	463K
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	463K
Reasoning	C	Runs well	21.0 tok/s	10895 ms	463K
RAG	C	Runs well	21.0 tok/s	16762 ms	463K

Inference speed

Falcon H1 1.5B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Falcon H1 1.5B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~29 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	28.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	24.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	24.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	21.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	21.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	21.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	21.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	21.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	21.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	21.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.0	Fits

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 Falcon H1 1.5B Instruct (1.5B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C49
Q3_K_S	3	0.7 GB	Low	C49
NVFP4	4	0.8 GB	Medium	C49
Q4_K_M	4	0.9 GB	Medium	C49
Q5_K_M	5	1.1 GB	High	C50
Q6_K	6	1.2 GB	High	C50
Q8_0	8	1.6 GB	Very High	C51
F16Best for your GPU	16	3.1 GB	Maximum	C53

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

Can GTX 1070 Ti 8GB run Falcon H1 1.5B Instruct?

Yes, GTX 1070 Ti 8GB can run Falcon H1 1.5B Instruct with a C grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Falcon H1 1.5B Instruct need?

Falcon H1 1.5B Instruct (1.5B parameters) requires approximately 3.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Falcon H1 1.5B Instruct?

The recommended quantization for Falcon H1 1.5B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Falcon H1 1.5B Instruct run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Falcon H1 1.5B Instruct achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run Falcon H1 1.5B Instruct for coding?

For coding workloads, Falcon H1 1.5B Instruct on GTX 1070 Ti 8GB receives a C grade with 21.0 tok/s and 463K context.

What context window can Falcon H1 1.5B Instruct use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Falcon H1 1.5B Instruct can safely use up to 463K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1070 Ti 8GB See all hardware for Falcon H1 1.5B Instruct

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