Can Falcon H1 Tiny 90M Instruct run on RTX 2070 8GB?

Q: What should I upgrade first if Falcon H1 Tiny 90M Instruct feels slow on RTX 2070 8GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

D36Poor

Estimated from fit model

Falcon H1 Tiny 90M Instruct needs ~2.2 GB VRAM. RTX 2070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~2 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth

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) — 2.2 GB, 2.0 tok/s, Runs well

2.2 GB required8.0 GB available

28% VRAM used

Fit status

Runs well

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

951K

Memory

2.2 GB / 8.0 GB

Memory breakdown

Weights0.1 GB

KV Cache0.1 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsFalcon H1 Tiny 90M Instruct on RTX 2070 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 2.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	D	Runs well	2.0 tok/s	52800 ms	476K
Coding	D	Runs well	2.0 tok/s	96800 ms	951K
Agentic Coding	D	Runs well	2.0 tok/s	140800 ms	1.9M
Reasoning	D	Runs well	2.0 tok/s	114400 ms	951K
RAG	D	Runs well	2.0 tok/s	176000 ms	1.9M

Inference speed

Falcon H1 Tiny 90M Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Falcon H1 Tiny 90M Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~2 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	2.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0

Quantization options

How Falcon H1 Tiny 90M Instruct (0.09000000357627869B params) fits at each quantization level on RTX 2070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.0 GB	Low	C48
Q3_K_S	3	0.0 GB	Low	C48
NVFP4	4

Get started

Copy-paste commands to run Falcon H1 Tiny 90M Instruct on your machine.

Run

lms load hf-tiiuae--falcon-h1-tiny-90m-instruct-gguf && lms server start

Frequently asked questions

Can RTX 2070 8GB run Falcon H1 Tiny 90M Instruct?

Yes, RTX 2070 8GB can run Falcon H1 Tiny 90M Instruct with a D grade (Runs well). Expected decode speed: 2.0 tok/s.

How much VRAM does Falcon H1 Tiny 90M Instruct need?

Falcon H1 Tiny 90M Instruct (0.09000000357627869B parameters) requires approximately 2.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Falcon H1 Tiny 90M Instruct?

The recommended quantization for Falcon H1 Tiny 90M Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Falcon H1 Tiny 90M Instruct run at on RTX 2070 8GB?

On RTX 2070 8GB, Falcon H1 Tiny 90M Instruct achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX 2070 8GB run Falcon H1 Tiny 90M Instruct for coding?

For coding workloads, Falcon H1 Tiny 90M Instruct on RTX 2070 8GB receives a D grade with 2.0 tok/s and 951K context.

What context window can Falcon H1 Tiny 90M Instruct use on RTX 2070 8GB?

On RTX 2070 8GB, Falcon H1 Tiny 90M Instruct can safely use up to 951K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2070 8GB See all hardware for Falcon H1 Tiny 90M Instruct

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