text model · Falcon · Windows

Can I run Falcon-H1-34B-Instruct on Nvidia GeForce RTX 5090 (32GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated ~61 tok/s est.

Yes. Falcon-H1-34B-Instruct runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~21.1 GB of ~31 GB usable).

Needs ~21.1 GB Device usable ~31 GB

Runs at Q4_K_M using ~21.1 GB of ~31 GB usable.

That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 5090 (32GB) leaves ~9.9 GB of headroom.

Q4_K_M needed: ~21.1 GB
Usable on device: ~31 GB
Device memory: 32 GB
Best quant: Q4_K_M

Share on X Share on Reddit

Which quant fits

Quant ladder vs ~31 GB usable

Q2_K

~16.4 GB

Q3_K_M

~18.8 GB

Q4_K_M

~21.1 GB

Q5_K_M

~26.4 GB

Q6_K

~30.1 GB

Q8_0

~35.5 GB

FP16

~69.5 GB

The line marks Nvidia GeForce RTX 5090 (32GB)'s ~31 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~575 W
Electricity / 1M tokens: ~$0.39
Pays for itself after: ~18,173M tok

At ~$0.15/kWh and the estimated ~61 tok/s, a million generated tokens costs about $0.39 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,999 Nvidia GeForce RTX 5090 (32GB) pays for itself after roughly 18,173 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands Windows

Pick your tool. All 2 load the same Q4_K_M weights.

llama.cpp

$ llama-cli -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M

LM Studio

$ lms get tiiuae/Falcon-H1-34B-Instruct-GGUF

How to run it

On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).

Model Falcon

Parameters: 34B
Q4_K_M size: 18.94 GB
Q8_0 size: 33.31 GB
Context: 256k

Full Falcon-H1-34B-Instruct requirements →

Device Windows

Memory: 32 GB vram
Usable for weights: ~31 GB
Power draw: ~575 W
Best runtime: vLLM (Linux) / Ollama (CUDA)

Best models for Nvidia GeForce RTX 5090 (32GB) →

You could also run

Run Falcon-H1-34B-Instruct on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)32GB RAM Laptop (CPU/iGPU only)Apple M4 Pro (48GB)Apple M5 Pro (48GB)

FAQ

Can Nvidia GeForce RTX 5090 (32GB) run Falcon-H1-34B-Instruct?

Yes. Falcon-H1-34B-Instruct runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~21.1 GB of ~31 GB usable).

How much memory does Falcon-H1-34B-Instruct need?

Nvidia GeForce RTX 5090 (32GB) has room to spare. At Q4_K_M the weights are ~18.94 GB; with KV cache and runtime overhead, budget ~21.1 GB at a 4k context.

What is the best tool to run Falcon-H1-34B-Instruct on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

Embed this

A live badge for your model card or README, updated as the data is.

Markdown

[![Falcon-H1-34B-Instruct on Nvidia GeForce RTX 5090 (32GB)](https://localmodel.run/badge/falcon-h1-34b/nvidia-rtx-5090-32gb.svg)](https://localmodel.run/can-i-run/falcon-h1-34b/nvidia-rtx-5090-32gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.