Key fact: An RTX 4090 fits Qwen3.6-27B at Q4 and does not fit a 70B model at Q4.
How we got it: 27 × 0.5 × 1.2 + 2 = 18.2 GB. Unsloth Q4_K_M file 16.82 GB on 2026-10-05. 70 × 0.5 × 1.2 + 2 = 44 GB. Card is 24 GB GDDR6X.
Checked:
NVIDIA RTX 4090 for Local LLMs — 24 GB That Fits 32B at Q4
By LLM Hardware team · Methodology ·
Drafted with AI assistance. The one measured speed is cited to the XiongjieDai llama.cpp runs; other rows are marked not measured.
Key facts
- VRAM
- 24 GB GDDR6X. Qwen3.6-27B Q4 is 18.2 GB and fits. 70B Q4 is 44 GB and does not.
- Price
- MSI Gaming X Trio B0BG94PS2F was currently unavailable on 2026-10-13. Check current price on Amazon.
- Versus a 5090
- Extra 8 GB is for Q5 and Q8 of a 24B MoE, not for 70B Q4. Check current price on Amazon.
- Checked
- 2026-10-13
Checked 2026-10-13. Author: LLM Hardware team. This site does not store or show Amazon prices. Check current price on Amazon.
Best for: people who want to run 27–32B models at Q4 on one card, fast. Not for 70B at Q4 (44 GB).
| VRAM | 24 GB GDDR6X (source) |
|---|---|
| Memory bandwidth | 1008 GB/s (source) |
| Llama 3 8B Q4_K_M generation | 127.7 tok/s (May 2024 runs) (source) |
| Largest model at Q4_K_M | 32B (21.2 GB = 32 × 0.5 × 1.2 + 2) |
| Board power / NVIDIA PSU | 450 W / 850 W |
| RTX 4090 | RTX 3090 | |
|---|---|---|
| VRAM | 24 GB | 24 GB |
| Bandwidth | 1008 GB/s | 936 GB/s |
| Llama 3 8B Q4_K_M | 127.7 tok/s | 111.7 tok/s |
| Power / PSU | 450 W / 850 W | 350 W / 750 W |
Pairs with: An 850 W PSU (NVIDIA's figure for the 4090), 32–64 GB system RAM, and a case that clears a 3–3.5-slot card. PSU, RAM and case sizing for an LLM build · VRAM calculator · Can I run it? · How much VRAM do I need?
Checked 17 October 2026. The MSI Gaming X Trio page (B0BG94PS2F) still said currently unavailable, so no new price is shown. 24 GB fits Granite Switch 4.1 30B at Q4 (20.0 GB) and Qwen3.6-27B Q4 (18.2 GB). It does not fit Reflection Beam at Q4 (302.8 GB) or Qwen3.8-Flash-Next (UD-Q4_K_XL 111.33 GB).
Citeable facts
Key fact: LFM2.5-24B-A2B Q4 is 16.4 GB and fits 24 GB. Q8 is 30.8 GB and does not. Granite Switch 4.1 30B Q4 is 20.0 GB and fits. Beam Q4 is 302.8 GB and does not.
How we got it: On 17 October 2026 ASIN B0BG94PS2F still returned the 24 GB Gaming X Trio title and a currently-unavailable notice. No new core price was recorded. 24 × 0.5 × 1.2 + 2 = 16.4. 29.99 × 0.6 + 2 = 20.0. 501.3 × 0.6 + 2 = 302.8.
Checked:
Methodology — parameter math, quantization bytes, and the source list.
The RTX 4090 is a 24 GB card for people who want to run 27–32B models at Q4 fast. It has 24 GB of GDDR6X at 1008 GB/s (NVIDIA Ada whitepaper) and generated 127.7 tok/s on Llama 3 8B Q4_K_M in the XiongjieDai llama.cpp runs (May 2024). Only the 32 GB RTX 5090 is faster among consumer cards. This guide covers what you can run, how fast it is, and whether the price tag is justified for your workload.
RTX 4090 launch MSRP re-read 17 October 2026 from the NVIDIA Newsroom announcement of 20 September 2022 (cards from 12 October 2022). The manufacturer launch MSRP is in the table below. On 17 October 2026 the Amazon page still said currently unavailable, so this page does not show an Amazon price.
Links to Amazon are affiliate links. No extra cost to you.
Buy on AmazonManufacturer MSRP (launch list price)
These are NVIDIA’s launch MSRPs from NVIDIA’s own pages, checked 17 October 2026. This is not Amazon’s price. Current prices are on Amazon, and partner cards often list higher.
| GPU | Manufacturer MSRP (launch list price) | Official source |
|---|---|---|
| GeForce RTX 5090 | $1,999 | NVIDIA: starting at $1,999, 30 Jan 2025 |
| GeForce RTX 5080 | $999 | NVIDIA: starting at $999, 30 Jan 2025 |
| GeForce RTX 5070 Ti | $749 | NVIDIA: starting at $749, February 2025 |
| GeForce RTX 5070 | $549 | NVIDIA: starting at $549, February 2025 |
| GeForce RTX 5060 Ti 16 GB | $429 | NVIDIA: 16 GB starting at $429, 16 Apr 2025 |
| GeForce RTX 4090 | $1,599 | NVIDIA Newsroom: starting at $1,599, 12 Oct 2022 |
Checked 17 October 2026 against the linked manufacturer pages. Not Amazon’s price; current prices are on Amazon.
TL;DR Verdict
The RTX 4090 is the best 24 GB card for speed; the RTX 5090 (32 GB) is the faster card overall. Its 1008 GB/s bandwidth is about 3.5× an RTX 4060 Ti 16 GB’s 288 GB/s (NVIDIA specs). The 24 GB VRAM ceiling handles Qwen3.6-27B at Q4 (18.2 GB) and 32B/34B at Q4. Llama 3.3 70B at Q4 is 44 GB and does not fit. This pass did not verify a Q2 file. For 70B at Q4 you need more memory (64 GB unified or two 24 GB cards). Buy it if speed on 7B–34B models is your priority. For 70B+ models, consider Mac Studio M4 Max or a dual-GPU setup instead.
RTX 4090 Specifications
| Specification | Value |
|---|---|
| VRAM | 24 GB GDDR6X |
| Memory bandwidth | 1,008 GB/s |
| TDP | 450W |
| Architecture | Ada Lovelace |
| Memory bus | 384-bit |
| CUDA cores | 16,384 |
| CUDA support | Full (CUDA 8.9) |
Full hardware details: RTX 4090 24GB hardware page.
Why the RTX 4090 Dominates LLM Inference
The RTX 4090 leads consumer-tier LLM inference on every llama-bench comparison we cross-checked (XiongjieDai community runs, Home GPU LLM Leaderboard, Hardware Corner GPU ranking). Its 24 GB GDDR6X and 1008 GB/s bandwidth let it run 13B models at FP16 and 34B models at Q4_K_M with the highest single-card throughput in the 24 GB tier.
LLM inference speed is almost entirely limited by memory bandwidth — the rate at which weights can be streamed from VRAM to the compute units for each generated token. The RTX 4090's 1008 GB/s is the defining number: 2.2x faster than the RTX 4060 Ti 16GB (288 GB/s), 1.4x faster than the RTX 3090 (936 GB/s), and 1.4x faster than the RTX 4080 Super (736 GB/s).
1,008 GB/s
Memory bandwidth
2.2x faster than RTX 4060 Ti 16GB at token generation
24 GB
GDDR6X VRAM
Fits every model up to 34B at Q4_K_M with room to spare
90–110 t/s
Peak range at 7B Q4
Cross-referenced with XiongjieDai llama-bench runs and the Home GPU LLM Leaderboard
The 24 GB VRAM also enables running multiple models simultaneously — load a 7B and a 14B model at once, for example, using Ollama's model caching. This is practical for users who switch between a fast chat model and a slower reasoning model without waiting for each to reload.
What Models Can the RTX 4090 Run?
The 24 GB ceiling fits Qwen3.6-27B at Q4 (18.2 GB) and a 34B model at Q4 (22.4 GB). Qwen3.6-35B-A3B at Q4 is 23.0 GB, so the Unsloth UD-Q4_K_M file (22.13 GB on 5 October 2026) loads with almost no extra context. Llama 3.3 70B at Q4_K_M is 44 GB and does not fit. This pass did not verify a Q2 file for that 70B model.
Run ollama run qwen3.6:27b for the dense model. In LM Studio, pick the Q4_K_M GGUF, not Q8 (the Unsloth Q8_0 file was 28.60 GB). ollama run granite4.2:30b matches IBM’s 17.72 GB Q4_K_M file. Do not point this card at Kolibri-1. See the VRAM calculator, the Qwen3.6 fit table, and the GPU buying guide before you treat a 32 GB 5090 as required for 27B.
| Model | Quant | VRAM needed | Speed (tok/s) | Fits? | Notes |
|---|---|---|---|---|---|
| Qwen3 4B | Q4_K_M | 4.4 GB | not measured | Yes | Large headroom |
| Qwen3 8B | Q4_K_M | 6.8 GB | not measured (Llama 3 8B: 127.7 tok/s) | Yes | Large headroom |
| Qwen3 14B | Q4_K_M | 10.4 GB | not measured | Yes | Room for long context |
| Qwen3 30B-A3B (MoE) | Q4_K_M | 20.0 GB | not measured | Yes | MoE: only ~3B active per token |
| Qwen3 32B | Q4_K_M | 21.2 GB | not measured | Yes | Near full 24 GB |
| DeepSeek-R1-Distill-32B | Q4_K_M | 21.2 GB | not measured | Yes | Reasoning distill; short context only |
| Llama 3.3 70B | Q4_K_M | 44 GB | not listed | Tight | 70 × 0.5 × 1.2 + 2 = 44 GB. No verified Q2 file in this pass. |
Source: the only measured speed on this page is Llama 3 8B Q4_K_M from the XiongjieDai community llama-bench runs (repo last updated May 2024). Other rows are marked not measured. Real-world speeds vary by driver version, context length, and batch size. Use the VRAM Calculator for exact numbers. Learn about quantization tradeoffs in the quantization guide.
RTX 4090 vs RTX 4080 vs Mac Studio M4 Max
The RTX 4080 16GB is the natural step-down. The Mac Studio M4 Max (64 GB) is the main competition for users who need larger models or prefer Apple silicon.
| Spec | RTX 4090 24GB | RTX 4080 16GB | Mac Studio M4 Max 64GB |
|---|---|---|---|
| VRAM | 24 GB | 16 GB | 64 GB |
| Memory bandwidth | 1,008 GB/s | 736 GB/s | 410 GB/s |
| TDP | 450W | 320W | ~45W |
| Max model at Q4_K_M | 34B | 13B | 70B+ |
| Tokens/sec at 7B Q4 | ~90 t/s | ~70 t/s | ~45 t/s |
| Tokens/sec at 32B Q4 | ~40 t/s | N/A (no fit) | ~20 t/s |
| Driver setup | CUDA | CUDA | None |
| Architecture | Ada Lovelace | Ada Lovelace | Apple M4 Max |
RTX 4090 vs RTX 4080: The 4090 costs more but delivers 37% more bandwidth and 50% more VRAM. Generation speed tracks bandwidth, but we have not measured the gap. The VRAM difference lets you fit 32B at Q4_K_M (21.2 GB) vs 20B (14.0 GB). If 16 GB is enough for your models, the 4080 is the cheaper choice. If you need 32B or more speed, the 4090 is worth the premium.
RTX 4090 vs Mac Studio M4 Max: In the XiongjieDai llama.cpp runs (May 2024, Llama 3 8B Q4_K_M) the 4090 made 127.7 tok/s vs 50.7 on an M3 Max 40-core; M4 Max was not in that set. The Mac Studio's 64 GB handles Llama 3.3 70B at Q4_K_M cleanly — something the 4090 cannot do without quality loss. The Mac Studio costs considerably more, but includes the full system (CPU, RAM, SSD, OS). For pure LLM speed below 24 GB, the 4090 wins. For 70B+ model quality, the Mac Studio wins.
Setting Up the RTX 4090 for LLM Inference
The RTX 4090 has full CUDA support (compute capability 8.9) and is plug-and-play with every major LLM inference tool. Below are the most common setups.
Ollama — quickest path to running models
Install Ollama and it detects the 4090 automatically. Pull and run any model with one command. The 4090's VRAM allows multiple models to stay loaded simultaneously.
# Install Ollama (Linux/macOS)
curl -fsSL https://ollama.com/install.sh | sh
# Run a fast 8B model
ollama run qwen3:8b
# Run a full 32B model — fits comfortably in 24 GB
ollama run qwen3:32b
# Qwen3.6-27B Q4 is 18.2 GB. Do not pull llama3.3:70b onto 24 GB.
ollama run qwen3.6:27b
Verify GPU usage: run ollama ps while a model is loaded — it will show VRAM usage and GPU utilization.
llama.cpp — direct control and multi-GPU support
Compile with CUDA for full GPU acceleration. The -ngl flag offloads all layers to GPU. For multi-GPU setups, use --tensor-split to distribute a 70B model across two 4090s.
# Single 4090 — run a 32B model fully on GPU
./llama-cli -m qwen3-32b-q4_k_m.gguf -ngl 99 -p "Hello"
# Dual 4090 — split a 70B model across two GPUs (48 GB total)
./llama-cli -m llama-3.3-70b-q4_k_m.gguf -ngl 99 --tensor-split 1,1
LM Studio
GUI interface for downloading and running GGUF models. Good for exploring models without terminal usage. Auto-detects 4090 and maximizes GPU layers.
vLLM
Best for serving models as an API endpoint with batching support. Requires PyTorch with CUDA. Ideal for multi-user setups where throughput matters more than latency.
For a full step-by-step walkthrough, see the how to run LLMs locally guide.
Should You Buy the RTX 4090?
The 4090 makes sense when your workload sits in the 7B-to-34B range and inference speed matters more than the absolute lowest price. Heavy users running models many hours a day, anyone keeping multiple models warm (a fast 7B chat model plus a 32B reasoning model like DeepSeek-R1, for example), all land in the 4090's sweet spot.
The 4090 stops being the right answer in two opposite directions. If you primarily care about Llama 70B+ at Q4_K_M, the 24 GB ceiling forces aggressive quantization — a Mac Studio M4 Max 64 GB handles 70B at Q4 cleanly. If you only run 7B models, the 4090 is overspending on bandwidth you will not use; a 12 GB card such as the RTX 4070 holds a 7–8B model at Q4 (6.8 GB) for a fraction of the cost. Power and acoustics matter too: 450W TDP needs an 850W+ PSU and serious case airflow, and a Mac mini M4 hits a similar 8B speed at roughly one-fifteenth the wall draw.
The verdict: Across the 24 GB consumer tier, the RTX 4090 ranks ahead of the RTX 3090 in the Home GPU LLM Leaderboard and the XiongjieDai llama-bench runs. 24 GB fits the practical range of non-70B models at Q4_K_M. The price is steep and the 450W TDP is real — factor in a quality PSU (850W+ recommended) and good airflow. Note: Llama 4 Scout requires ~58–62 GB at Q4_K_M due to its MoE architecture — a single RTX 4090 cannot run it. For Scout, you need two RTX 4090s (48 GB combined, with CPU offloading) or a Mac Studio M4 Max 64GB. For everything below 70B on a single consumer card, the RTX 4090 leads on benchmarked throughput. See the full GPU buying guide for all budget tiers.
Frequently Asked Questions
Is the RTX 4090 worth it for LLMs?
Yes, if you want fast 24 GB inference and can pay for it. The RTX 4090 has 1008 GB/s of memory bandwidth (NVIDIA Ada whitepaper) and generated 127.7 tok/s on Llama 3 8B Q4_K_M in the XiongjieDai llama.cpp runs. The 32 GB RTX 5090 is faster (1,792 GB/s), and a used RTX 3090 is about 13% slower for much less. The price is steep, so it makes most sense if you run inference many hours a day.
Can the RTX 4090 run 70B models?
Not at Q4_K_M. Llama 3.3 70B at Q4_K_M is 70 × 0.5 × 1.2 + 2 = 44 GB, which does not fit in 24 GB. This 5 October 2026 check did not verify a Q2 file that fits, so Q2 is not a recommended 70B path on one 4090. Qwen3.6-27B at Q4 is 18.2 GB and does fit. Qwen3.6-35B-A3B at Q4 is 23.0 GB, and Unsloth’s UD-Q4_K_M file was 22.13 GB, so the card can load it with almost no extra context. For a comfortable 70B Q4, use 64 GB of unified memory or two 24 GB cards.
RTX 4090 vs Mac Studio for LLMs — which is better?
It depends on your workload. The RTX 4090 wins on speed for 7B–34B models: 1008 GB/s bandwidth vs ~410 GB/s for the Mac Studio M4 Max. No M4 Max speed is listed here; in the XiongjieDai llama.cpp runs an M3 Max 40-core made 50.7 tok/s vs 127.7 on the 4090 (Llama 3 8B Q4_K_M). The Mac Studio M4 Max with 64 GB wins on model size: it can run Llama 3.3 70B at Q4_K_M (44 GB), which the 4090 cannot. For pure speed on models under 24 GB, RTX 4090 wins. For 70B+ models, Mac Studio wins.
Can I run multiple 4090s for larger models?
Yes. Two RTX 4090s give you 48 GB of combined VRAM, enough for Llama 3.3 70B at Q4_K_M (44 GB). llama.cpp splits a model across GPUs with the --tensor-split flag, and Ollama uses multiple GPUs automatically on Linux. A dual-4090 system needs a motherboard with two PCIe x16 slots and a power supply sized for two 450W cards.
What is the best GPU for running LLMs locally in 2026?
The RTX 4090 is the strongest 24 GB consumer GPU for local LLMs: 24 GB GDDR6X at 1008 GB/s (NVIDIA Ada whitepaper). The RTX 5090 is faster and has 32 GB, at a higher price. For 70B+ models, the Mac Studio M4 Max (64 GB) or Mac Studio Ultra (192 GB) provide larger memory pools at higher cost. On a tighter budget, the RTX 4070 Ti Super 16 GB or RTX 4060 Ti 16 GB offer 16 GB at lower prices, with less bandwidth.
Related Resources
RTX 4090 24GB hardware page
Full specs, pricing history, and VRAM compatibility details
Best GPU for LLMs — Full Guide
Every budget tier ranked for local LLM inference
RTX 4060 Ti 16GB Guide
Best VRAM-per-dollar NVIDIA GPU
LLM Quantization Explained
Q4 vs Q8 vs FP16 — when quality tradeoffs matter
How to Run LLMs Locally
Step-by-step setup with Ollama, LM Studio, and llama.cpp
Run Qwen3 Locally
Qwen3 32B at Q4 fits on the RTX 4090 — full setup guide
Qwen3 Hardware Requirements
RTX 4090 can run Qwen3 32B — best model for 24 GB
Dual GPU LLM Guide
Two RTX 4090s = 48 GB — run 70B and Llama 4 Scout
How to Run Llama 4 Scout Locally
Scout needs 58 GB — dual 4090 or Mac Studio 64GB setup guide
VRAM Calculator
Check exact VRAM requirements for any model and quantization
RTX 4070 vs 4080 vs 4090 Comparison
Three-way GPU comparison — 12 GB vs 16 GB vs 24 GB for local LLMs
RTX 5080 vs RTX 4090 Comparison
16 GB Blackwell vs 24 GB Ada — which to buy in 2026
RTX 5090 vs RTX 4090
Upgrade to Blackwell? 78% faster, 32 GB vs 24 GB — buy verdict.
Best LLMs for 24 GB VRAM
Top model picks for your RTX 4090
Check VRAM requirements for any model, or compare the RTX 4090 against other hardware.
Related Guides
Sources & methodology
VRAM figures on this page come from the site’s VRAM formula and model cards. The sitewide formula and the full source list are on the methodology page. For this guide we used:
- XiongjieDai GPU-Benchmarks-on-LLM-Inference. RTX 4090 llama-bench numbers across 7B, 13B, 34B and 70B-offload runs.
- Hardware Corner GPU ranking. 4090 tokens per second at multiple context lengths for the speed tables here.
- Home GPU LLM Leaderboard. 24 GB VRAM tier listings that put the 4090 in context against the 3090 and 5090.
Spot a number that does not match the linked source? Contact us and we will update the guide.