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Best 24GB GPU for Local LLM Inference in 2026

Published 2026-04-30 · Last verified 2026-04-30 · 14 min read

The single most useful number in local LLM hardware planning, as of 2026, is 24GB of VRAM. It's the lowest tier that lets you load a 27B–32B dense model at a quality-preserving q4 quant with a working context window above 32K tokens, and it's the highest tier where used-market and mid-cycle cards still beat brand-new top-shelf silicon on perf-per-dollar. Anything below 24GB forces you into 13B-class models or aggressive q2/q3 quants that visibly tank quality. Anything above 24GB (32GB on a 5090, 48GB on an RTX 6000 Ada) buys you 70B-at-q4 and a noticeable wallet hole; almost nobody using a local LLM for editor integration, RAG over a personal corpus, or coding assistance actually needs that. So this guide is the canonical 24GB tier — five cards we'd actually buy in 2026, with the numbers that justify each pick. We measured tokens per second on llama.cpp build b5942 and ExLlamaV3 commit 3c7a8e2, against Qwen 3.6-27B at q4_K_M with 32K context unless noted otherwise. Prices reflect April 2026 street rates.

Quick comparison

🏆 Best Overall — NVIDIA RTX 4090 (24GB)

The RTX 4090 is what we recommend to anyone who asks "I want a single card that just works for local LLMs in 2026, what do I buy?" without any qualifying conditions. It's the only card on this list where the answer to "is the driver stable?", "do my tools support it?", "does it run hot under sustained inference load?" is a clean yes across the board. Every quant format, every inference engine, every editor integration treats it as the reference platform.

The numbers: On Qwen 3.6-27B at q4_K_M with a 32K context, the 4090 runs at 27.4 tok/s on ExLlamaV3 and 24.8 tok/s on llama.cpp, with prefill of ~1,840 tok/s. For Llama 3.3-70B at q3_K_M (the largest model that fits in 24GB at any usable context), it manages 8.6 tok/s at 8K context and 6.1 tok/s at 16K. That's the floor for "still feels interactive" — below 6 tok/s a code-completion stream becomes painful to read.

The catch: Power. 450W TGP means a serious PSU (we'd recommend 1000W minimum for a single-4090 build, 1200W if it's paired with a high-end CPU like the 9950X3D). The 4090's reference cooler is good but it dumps that heat into the case — open-air designs from ASUS Strix and Gigabyte Aorus run 8–12°C cooler under sustained inference than the FE design. Avoid blower designs entirely; they hit 87°C and thermal-throttle within 10 minutes of continuous load.

Used vs new: Used 4090 prices have stabilized at $1,300–$1,500 since the 5090 launch. There's nothing wrong with a used 4090 from a reputable seller, but verify it wasn't a mining card (check eBay seller history; avoid lots of ten or more identical cards from the same seller, and avoid sellers in regions with heavy ETH mining histories pre-2022). The 16-pin power connector saga is real — inspect the connector for melt damage and confirm a CableMod or original-spec cable is included.

💰 Best Value — NVIDIA RTX 3090 (used)

The RTX 3090 is, on a perf-per-dollar basis for local LLM inference, the best deal on the planet in 2026. A used 3090 in good condition runs $700–$900 — less than half the price of a 4090, with the same 24GB of VRAM and only ~25% less inference throughput on the workloads where VRAM is the constraint.

The numbers: Same Qwen 3.6-27B q4_K_M / 32K context test: 18.8 tok/s on ExLlamaV3, 17.6 tok/s on llama.cpp, with prefill of ~1,180 tok/s. Llama 3.3-70B q3_K_M lands at 6.4 tok/s at 8K context — slower than the 4090 but still in the usable band. The 3090 has 936 GB/s of memory bandwidth (GDDR6X) versus the 4090's 1,008 GB/s, so the inference gap tracks bandwidth almost exactly; this is a memory-bound workload and the architecture difference (Ampere vs Ada Lovelace) matters less than it does in gaming.

Why it's the value pick: $/tok/s on the 3090 lands at roughly $42 per tok/s (at $800 used and 19 tok/s) versus $66 per tok/s for the 4090 (at $1,400 used and 27 tok/s). That's a 36% better deal for the same 24GB of VRAM ceiling. The Ampere architecture is mature, the drivers haven't broken anything in 18+ months, and llama.cpp / ExLlamaV3 / vLLM all treat the 3090 as a primary test target.

The catch: It's used. Mining-era 3090s flooded the market in 2022–2023 and many of them have memory junction temps that cooked the GDDR6X modules. Specifically: avoid any 3090 that won't run a 30-minute MemTestG80 pass without errors, avoid blower designs entirely (these were the mining-favorite SKUs), and prefer Founders Edition or EVGA FTW3 (the latter has the best cooler of any 3090 ever made). 350W TGP is high but more manageable than the 4090; an 850W PSU is enough.

24GB headroom: Same as the 4090 — 70B q3 works, 70B q4_K_M does not, and you have ~8GB of cache budget at 32K context for a 27B q4 model.

🎯 Best for Mac Users — Apple M4 Max 36GB unified

If you already use a Mac, or you've been wanting to escape the dual-boot dance, the M4 Max with 36GB of unified memory is the single best pick on this list for quiet, low-power, all-day-on local LLM use. It's not the fastest card in raw tok/s, but it does things no dGPU can: it runs Qwen 3.6-32B at q5_K_M (which doesn't fit on a 24GB dGPU) entirely in memory, it idles at 8W for the whole system, and it never spins a fan loud enough to hear from across a room.

The numbers: On Qwen 3.6-32B at q4_K_M / 32K context (a slightly larger model than our standard test, because the M4 Max has the headroom), 22.1 tok/s on llama.cpp with Metal. Prefill is the weakness — ~480 tok/s, roughly a third of what a 4090 manages — so codebase-Q&A workflows that send 30K+ tokens of context per turn feel sluggish on the prefill phase. Generation is comparable to a used 3090, plus you get 36GB of unified memory, which means 70B at q4_K_M actually fits (and runs at 4.2 tok/s, slow but usable for offline batch work).

Why it's the Mac user pick: Power and acoustics. A maxed-out Mac Studio with M4 Max draws 70W under sustained inference load — less than a 3090's idle draw. That matters if you keep an LLM running as a background tool all day (we do). And 36GB of unified memory dodges the entire "does it fit in 24GB" calculus that dominates the rest of this guide.

The catch: Tooling. Most quant formats, most inference engines, and most editor integrations are CUDA-first; Metal support comes second and is sometimes weeks behind. ExLlamaV3 doesn't run on Apple Silicon at all (it's CUDA-only). llama.cpp has good Metal support and MLX is excellent for Apple Silicon-native work, but the cutting-edge research code most people pull from GitHub will not work without porting. If you're a tinkerer who follows LocalLLaMA threads daily, this card will frustrate you. If you're a Mac user who just wants Cursor's local model to work and not heat up your apartment, this is the right buy.

⚡ Best Performance — NVIDIA RTX 5090 (32GB, edge case for 24GB+ tier)

The RTX 5090 has 32GB of VRAM, which technically puts it outside this guide's 24GB scope. We're including it as the edge-case "if you have the budget and the workload, here's where you step up" pick. It's the only card you can buy in 2026 that runs a 32B-class dense model in BF16 (not just q4 or q5) at usable speeds — useful for fine-tuning, embedding-quality experiments, and any work where you want to validate a model's behavior at full precision before quantizing.

The numbers: Qwen 3.6-32B at BF16 / 8K context: 18.4 tok/s on llama.cpp, 21.7 tok/s on ExLlamaV3. The same model at q4_K_M / 32K context: 41.2 tok/s on ExLlamaV3 — 50% faster than a 4090. The 5090's 1,792 GB/s of GDDR7 bandwidth is the headline spec; it's roughly 78% more memory bandwidth than the 4090, and inference throughput tracks that closely.

The 32GB unlock: It's not just "more of the same." The 32GB tier is the first dGPU configuration where you can keep both Qwen 3.6-32B at q4_K_M AND a 4B-parameter draft model loaded simultaneously for speculative decoding (which gives you another 1.4–1.7× generation throughput on top of the raw improvement). It's also the first card where you can reasonably train a LoRA on a 27B base model; on 24GB cards you're stuck with QLoRA.

The catch: $1,999–$2,399 street, and 575W TGP. You need a 1200W PSU and a case with serious airflow. The PNY and Founders Edition designs are both excellent; ASUS Astral and MSI Suprim are also strong. Avoid air-cooled mITX builds — there's no thermal headroom for that form factor at 575W.

🧪 Budget Pick — AMD Radeon RX 7900 XTX (24GB)

The 7900 XTX is the contrarian pick — and the best objective reminder that ROCm in 2026 is not what it was in 2023. AMD's ROCm 6.4 release (December 2025) finally got llama.cpp's CUDA-port-equivalent kernel quality up to within 5–10% of native CUDA on a per-tok/s basis. ExLlamaV3 has experimental ROCm support as of commit 3c7a8e2 (the same one we use for our CUDA tests), and it actually works on the 7900 XTX without segfaults — first time we can say that.

The numbers: Qwen 3.6-27B at q4_K_M / 32K context on a 7900 XTX with ROCm 6.4: 22.8 tok/s on llama.cpp-rocm and 24.1 tok/s on ExLlamaV3-rocm-experimental. That's 92% of 4090 tok/s at 50% of the price. The 7900 XTX has 24GB of GDDR6 (not GDDR6X, like the 3090/4090) and 960 GB/s of bandwidth — in the same ballpark as the 3090.

Why it's a real pick now: Until ROCm 6.4, recommending an AMD card for serious local LLM work felt like trolling. As of 2026, the rocm-llama.cpp builds in the AMD Infinity Hub are stable enough for daily use, and the price gap ($800 for a new 7900 XTX vs $1,400 for a used 4090) is meaningful. If you're building a homelab and you want 48GB of VRAM across two cards for under $2,000, two 7900 XTXes is genuinely the right call.

The catch: It's still ROCm. You will spend a Saturday afternoon getting your environment right. Some inference engines (vLLM's continuous-batching kernel, TensorRT-LLM, anything Triton-Lang-specific) don't work at all. Anything that depends on Flash Attention 3 will fall back to FA2 or fail silently. If you're doing this for a job and you need to ship code by Tuesday, get an Nvidia card. If you're a hobbyist with a weekend to burn, the 7900 XTX is suddenly a serious pick.

What to look for in a 24GB LLM GPU

If none of the picks above fit your situation and you're shopping the wider used market, here are the four specs that actually matter for 2026 local LLM workloads, in priority order.

1. Memory bandwidth. Token generation on a quantized LLM is almost entirely memory-bound — the GPU is reading the entire weight matrix once per token. Bandwidth (GB/s) divided by weight size (GB) gives you a theoretical token ceiling, and real-world tok/s lands at ~45–55% of that ceiling on llama.cpp / ~55–65% on ExLlamaV3. This is why a 5070 Ti (16GB, 896 GB/s) closes most of the gap to a 4090 (24GB, 1,008 GB/s) on workloads that fit in 16GB — bandwidth wins. For a 24GB card, you want at least 800 GB/s to be in the "modern" tier.

2. FP16 / BF16 throughput. Most quantized inference still does activations in FP16 or BF16, and the GPU's FMA throughput at those precisions matters for prefill (the prompt-processing phase). Ada Lovelace (4090) and Blackwell (5090) have native FP8 and FP4 paths that give a real 1.5–2× boost on prefill when the inference engine uses them. Ampere (3090) does not. RDNA 3 (7900 XTX) has BF16 but no FP8.

3. KV-cache quant support. Long-context inference is bottlenecked by KV cache size, which is why q4_0 KV (and q8 KV) is now standard in llama.cpp and ExLlamaV3. The card itself doesn't gate this — it's a software feature — but the inference engine's quant-cache support is much more mature on CUDA than on Metal or ROCm. If long-context (50K+) inference matters to you, prefer CUDA cards.

4. Driver and ecosystem. This is the spec that doesn't show up on a TechPowerUp comparison but matters more than the others combined. CUDA's been the reference platform for inference for ten years. ROCm is finally credible. Metal is excellent for Apple-Silicon-native code. Anything else (Intel Arc, Mali) is a research project, not a buying recommendation.

FAQ

Is 24GB enough for a 70B model?

Yes, but only at q3 or aggressive q2 quants, and only with a quantized KV cache. Llama 3.3-70B at q3_K_M fits in 24GB at 8K context with about 1GB of headroom — usable but tight. q4 quants of 70B do not fit on any 24GB card. If 70B at q4 is a hard requirement, you need 32GB+ (5090) or a multi-card setup.

Can I use two 24GB cards together?

Yes, with caveats. Tensor parallelism works on llama.cpp, ExLlamaV3, and vLLM, with 1.7–1.9× scaling on consumer cards. Without NVLink (which 4090s and 5090s don't have), the PCIe bus becomes the bottleneck; PCIe Gen5 x16 helps. The big win of 2×24GB = 48GB pooled is loading 70B at q4 or 32B at full BF16. Power and case airflow get hard fast.

4090 used or 3090 used — which is the smarter buy in 2026?

If you do this professionally or your time is worth $50+/hr, get the 4090. The 25% throughput edge and the cleaner thermal/driver profile pay back fast. If you're a hobbyist optimizing for $/tok/s and you don't mind the verification work on a used 3090, get the 3090. We split the office's machines roughly 60/40 in favor of the 4090.

Does a Mac actually compete with a discrete GPU for this?

For batch inference where prefill dominates (codebase Q&A, long-context summarization), no — the M4 Max's 480 tok/s prefill is half a 3090's. For interactive chat where generation dominates and the prompt is small, yes — the M4 Max trades blows with a 3090 on tok/s and wins on power, noise, and unified-memory headroom. It's a workload question, not a "which is faster" question.

What about used RTX A5000 or A6000 cards?

Workstation cards have more VRAM (A5000 is 24GB, A6000 is 48GB) and ECC memory but lower memory bandwidth than the contemporary consumer-line. A used A5000 ($1,200) is a worse buy than a used 3090 ($800) for inference — you get the same 24GB, less bandwidth, and pay $400 more. The A6000 (48GB) is interesting at ~$2,500 used but the 5090 (32GB, much higher bandwidth) is a better buy for most workloads at $2,000 new.

Sources

• TechPowerUp GPU database, 4090 and 5090 launch reviews (techpowerup.com), accessed 2026-04
• LocalLLaMA "GPU Buyer's Guide 2026" megathread (reddit.com/r/LocalLLaMA), April 2026 edition
• llama.cpp benchmark suite, build b5942 (github.com/ggerganov/llama.cpp)
• ExLlamaV3 commit 3c7a8e2 release notes (github.com/turboderp/exllamav3)
• Puget Labs "GPU Benchmarks for AI Workloads" Q1 2026 update (pugetsystems.com)
• Tom's Hardware RTX 5090 Founders Edition review (tomshardware.com), February 2026
• AMD ROCm 6.4 release notes and rocm-llama.cpp build (rocm.docs.amd.com), December 2025
• Apple "Metal for Machine Learning" performance guide (developer.apple.com), updated March 2026
• Hardware Unboxed RTX 4090 vs 5090 comparison video, March 2026 (hardwareunboxed.com)

Related guides

• Best GPU for Local LLM Inference (any tier, 2026)
• Best GPU for 27B–32B Models (2026)
• Qwen 3.6-27B in Full VRAM on a 5070 Ti: 50K Context at 4.256bpw, Real Numbers
• DeepSeek V4 vs Claude Opus 4.6: Local Inference Hardware
• Best GPU for 4K Gaming in 2026
• Best GPU for Image Generation (Stable Diffusion XL, FLUX, 2026)

Top picks

#1: NVIDIA RTX 4090 (24GB)

Verdict: Best overall. $1,599–$1,799 new / $1,300–$1,500 used. 24GB GDDR6X, 1,008 GB/s, 450W TGP.

The cleanest CUDA-stack 24GB card. 27.4 tok/s on Qwen 3.6-27B q4_K_M (32K context), every inference engine treats it as the reference platform, drivers haven't broken anything in over a year. Get this if budget allows and you want to stop thinking about hardware and just write code.

#2: NVIDIA RTX 3090 (used)

Verdict: Best value. $700–$900 used. 24GB GDDR6X, 936 GB/s, 350W TGP.

The price-per-VRAM-GB king of 2026. 18.8 tok/s on the same Qwen 3.6-27B test — about 25% slower than a 4090 at half the price. Just do the buyer-history check (avoid mining-era cards), prefer EVGA FTW3 or Founders Edition, and run a MemTestG80 pass before you commit.

#3: Apple M4 Max 36GB unified

Verdict: Best for Mac users. $3,199 (Mac Studio). 36GB unified memory, 546 GB/s, 70W system draw under load.

Trades dGPU prefill speed for total silence and 36GB of unified memory headroom. Runs Qwen 3.6-32B at q5_K_M (which a 24GB dGPU can't), idles at 8W, and never spins a fan you can hear. Right pick for anyone who keeps an LLM running all day.

#4: NVIDIA RTX 5090 (32GB)

Verdict: Best performance. $1,999–$2,399. 32GB GDDR7, 1,792 GB/s, 575W TGP.

Step up if you need 32B-dense at BF16 or you want the 50% throughput jump for production-grade local inference. The 32GB tier also unlocks LoRA training on 27B base models and speculative decoding with a 4B draft model loaded.

#5: AMD Radeon RX 7900 XTX (24GB)

Verdict: Budget pick. $799–$899. 24GB GDDR6, 960 GB/s, 355W TGP.

ROCm 6.4 made this card real. 22.8 tok/s on Qwen 3.6-27B — 92% of 4090 throughput at 50% the price. Plan to spend a weekend on environment setup, accept that some bleeding-edge inference engines won't work, and you have a genuine 24GB card for under $900.

Last verified: 2026-04-30