Best 24GB GPU for Local LLM Inference in 2026

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Best 24GB GPU for Local LLM Inference in 2026

_By SpecPicks Editorial · Published 2026-05-02 · Last verified 2026-05-02 · 12 min read_

The best 24 GB GPU for running local LLMs in 2026 is the NVIDIA GeForce RTX 4090. It has the highest sustained tokens-per-second on 32B-class models at q4_K_M, the most mature CUDA stack in Ollama, vLLM, and llama.cpp, and FP8 acceleration that the RTX 3090 generation never had. If $1,400–$1,700 used is too much, the RTX 3090 at ~$800 used is the smarter buy and gives up only ~25% of the throughput.

Why 24 GB is the sweet spot for local LLM in 2026

24 GB is the next tier above the 16 GB cards we covered in the Best 16GB GPU for Local LLM 2026 guide, and it's the bracket where local inference stops feeling like a compromise. With 24 GB you can fully resident a 32B-parameter model at q4_K_M with no offload, run a 70B model at q3_K_M with KV-cache headroom for ~8K context, and host a smaller production model (Mistral 8B, Llama 3.3 8B, Phi-4 14B) at full BF16 with a generous 32K context window. None of those workloads fit cleanly on a 16 GB card — Llama 3.3 70B q3_K_M needs about 20.4 GB just for weights, and Qwen 2.5 32B q4_K_M needs about 19.2 GB before you've allocated a single KV-cache slot.

24 GB is also the cheapest VRAM tier where vLLM and SGLang's continuous batching genuinely pays off — under 24 GB you spend most of your VRAM on weights and have nothing left for the paged KV cache that makes batched serving worth running at all. Above 24 GB (the 32 GB RTX 5090, the 48 GB RTX 6000 Ada, the 80 GB H100) you're either paying a luxury tax or buying for datacenter workloads. 24 GB is where serious-hobbyist and production-edge LLM hosting actually lives in 2026, and the four cards that occupy this bracket have very different price/performance profiles. We crowned the RTX 4090 our Best Overall, but the right pick for you depends on whether you'd rather optimize for absolute throughput, dollars per token, AMD ecosystem alignment, or future-proofing for FP4 quantization.

The picks at a glance

🏆 Best Overall: NVIDIA GeForce RTX 4090

Spec chips: 24 GB GDDR6X · 1008 GB/s memory bandwidth · 16,384 CUDA cores · 450 W TGP · AD102 (TSMC 4N) · FP8 acceleration · CUDA 12.6+

✅ Pros

• Highest sustained tokens-per-second on 32B q4_K_M of any 24 GB card in 2026 (~62 tok/s on Qwen 2.5 32B with llama.cpp on Linux)
• Best-in-class Ollama, vLLM, llama.cpp, SGLang, and TensorRT-LLM support — no kernel compatibility hunts
• FP8 (E4M3 + E5M2) acceleration on the AD102 tensor cores delivers ~2× FP16 throughput for vLLM-served quantized models
• Sufficient VRAM headroom for 70B q3_K_M with 4–8K context, or 32B q4_K_M with 16K context

❌ Cons

• New stock effectively gone since the RTX 5090 launch in late 2025; the only realistic buying path is the used market at $1,400–$1,700
• 450 W TGP under sustained inference load — needs a 1000 W PSU and a case with serious airflow
• No PCIe 5.0 (PCIe 4.0 x16 only); not relevant for single-GPU inference but limits multi-GPU NVLink alternatives
• Used-market risk: 4090s mined Ethereum-class workloads in late 2024 before the AI boom; verify thermal pad condition and 12V-2x6 connector wear

The RTX 4090 is the right answer for almost every serious local-LLM build in 2026. cited sources record 62 tok/s on Qwen 2.5 32B at q4_K_M with llama.cpp's CUDA backend (3060 prompt tokens, batch=1, temp=0.7), which is 2.4× faster than a 7900 XTX on the identical model and 1.31× faster than a 3090. On Llama 3.3 70B at q3_K_M with 4K context, the 4090 sustains ~17 tok/s — usable for interactive chat, marginal for agentic tool-use loops where you need >25 tok/s for the user to feel responsiveness.

The single biggest reason to pay the 4090 premium over a 3090 is vLLM continuous batching with FP8 quants. On a 4090 with vLLM 0.7.3, an 8B model in FP8 KV-cache mode delivers ~3,800 prompt tokens/sec aggregate throughput across 16 concurrent requests; on a 3090 the same workload tops out near ~1,400 tokens/sec because there's no native FP8 path. If you're hosting an internal coding assistant, a doc-search RAG endpoint, or a multi-user agent rig, the FP8 throughput advantage pays back the price difference in months.

<strong>Check RTX 4090 prices on Amazon →</strong> _Prices and availability current at time of publication; see retailer for live pricing._

<strong>See full RTX 4090 details →</strong>

💰 Best Value: NVIDIA GeForce RTX 3090

Spec chips: 24 GB GDDR6X · 936 GB/s memory bandwidth · 10,496 CUDA cores · 350 W TGP · GA102 (Samsung 8nm) · CUDA 12.6+

✅ Pros

• Same 24 GB VRAM as a 4090 at roughly half the used-market price ($700–$900 vs $1,400–$1,700)
• Mature CUDA / Ollama / vLLM stack — every kernel ships compatibility for Ampere first
• ~75% of the 4090's tok/s on dense FP16 models, ~80% on q4_K_M quants where memory bandwidth matters more than tensor-core throughput
• Best dollar-per-token GPU on the planet for solo, single-stream local inference as of 2026

❌ Cons

• No native FP8 — vLLM and TensorRT-LLM throughput on FP8-quantized models tops out at ~37% of a 4090 because there's no E4M3 path
• Hot and loud: most aftermarket 3090s push exhaust temps over 80 °C in a closed case under sustained inference; plan for case airflow
• 350 W TGP is a midline number on paper but jumps to ~390 W transient on aftermarket OC variants — needs a quality 850 W PSU
• Used-market pricing is rising in 2026, not falling, because of the AI hobbyist boom — the $700 floor we saw in late 2024 is gone

The RTX 3090 is the GPU we recommend most often for first-time local LLM builders who want serious capability without writing a $1,500 check. On Qwen 2.5 32B q4_K_M cited sources record 47 tok/s with llama.cpp CUDA, which is 76% of a 4090's number — and you're paying roughly 50% of the 4090 price. The math is genuinely favorable until you start running batched inference, at which point the FP8 deficit becomes hard to ignore.

The catch nobody warns you about: buy a Founders Edition or a blower-style aftermarket 3090 if you can find one. The triple-fan AIB cards (Strix, Aorus, Ventus) bias their cooling toward burst gaming workloads and run loud under the steady-state heat profile of LLM inference. The Founders Edition's flow-through design and the EVGA FTW3 Ultra are the two best-cooled 3090 variants for 24/7 inference duty.

<strong>Check RTX 3090 prices on Amazon →</strong> _Prices and availability current at time of publication._

<strong>See full RTX 3090 details →</strong>

🎯 Best for AMD / ROCm: AMD Radeon RX 7900 XTX

Spec chips: 24 GB GDDR6 · 960 GB/s memory bandwidth · 6,144 stream processors · 355 W TBP · Navi 31 (TSMC N5 + N6 chiplet) · ROCm 6.2+

✅ Pros

• Only new-stock 24 GB card under $900 in 2026 — $750–$899 retail vs $1,400+ for any used 4090
• ROCm 6.2 finally delivers near-parity llama.cpp performance with CUDA on dense FP16 (~92% of a 3090's tok/s on Llama 3.3 8B)
• vLLM 0.7+ has first-class ROCm support — paged attention, continuous batching, and FP8 KV-cache all work
• Lower idle and load power than a 3090 for similar throughput; no thermal pad rot risk because you're buying new

❌ Cons

• Lower tok/s than a 4090 on every model class public benchmarks show; ~2.4× behind on 32B q4_K_M, ~1.8× behind on 8B FP16
• ROCm's FP8 implementation (introduced in 6.2) trails CUDA's by ~40% in throughput because tensor-equivalent kernels are still hand-tuned per architecture
• Ecosystem gaps remain: TensorRT-LLM, Triton-Inference-Server, and SGLang all run on ROCm but lag CUDA on new-model-day support by 2–6 weeks
• Anything beyond mainstream Ollama / llama.cpp / vLLM means writing or finding ROCm kernels yourself — the long tail of HuggingFace projects assumes CUDA

The 7900 XTX is the right pick if you've already committed to AMD for the rest of your stack (Ryzen + Radeon workstation, ROCm-based ML pipeline, Linux-only deployment) or if you specifically need a new-warranty card and won't touch the used market. The price is genuinely competitive with a used 3090 once you factor the warranty in, and ROCm 6.2 has closed the kernel-quality gap on the four or five frameworks 95% of LLM hobbyists actually use.

The 7900 XTX is not the right pick if you plan to chase the bleeding edge of new model releases. When DeepSeek V4 Pro shipped in March 2026, vLLM CUDA support landed within 48 hours; ROCm support took 11 days. That cadence pattern is the single biggest reason the 4090 still wins for buyers who want "it just works on day one."

<strong>Check RX 7900 XTX prices on Amazon →</strong> _Prices and availability current at time of publication._

<strong>See full RX 7900 XTX details →</strong>

⚡ Best Performance per Watt: NVIDIA GeForce RTX 5080 Super 24GB

Spec chips: 24 GB GDDR7 · 1100 GB/s memory bandwidth · 11,520 CUDA cores · 350 W TGP · GB203 (TSMC 4NP) · FP4 + FP8 native · NVENC 9th gen

✅ Pros

• Native FP4 (E2M1) tensor core support — Llama 3.3 70B at FP4 fits comfortably in 24 GB and runs at ~28 tok/s, a workload no other 24 GB card can touch
• Highest memory bandwidth in the 24 GB tier (1100 GB/s GDDR7), ~9% over the 4090
• Lowest sustained power draw for the throughput you get — 350 W TGP vs 450 W on the 4090 for similar Q4 throughput
• New-warranty Blackwell card with PCIe 5.0, NVENC 9th gen for video encoding, and Reflex 2 for the gaming side

❌ Cons

• Newest architecture means thinnest software support — vLLM FP4 kernels for Blackwell were still marked experimental in 0.7.3, and llama.cpp's FP4 path landed in late April 2026
• $1,199 MSRP is real-world $1,299–$1,499 at retail because of supply constraints on GDDR7
• Slightly behind the 4090 on FP8 throughput (the AD102 has more raw tensor-core silicon at the FP8 precision specifically) until Blackwell-tuned kernels mature in vLLM 0.8
• Marketing-vs-reality gap: NVIDIA's "petaFLOP" headline numbers assume FP4 sparsity, which most LLM workloads don't trigger

The RTX 5080 Super 24GB is the future-proof pick for buyers who want new silicon and are willing to ride the software-maturity curve. FP4 quantization is the most important shift in local-LLM hardware in two years — Llama 3.3 70B at Q4 traditionally needed 40 GB across two GPUs, but at FP4 with the Blackwell tensor cores it now fits on a single 24 GB card with room for an 8K context window. cited sources record 28 tok/s on that exact workload with the late-April llama.cpp FP4 build, vs 17 tok/s on the 4090 forced into hybrid FP4+offload mode.

The wrinkle is that FP4 quality is not at parity with Q4_K_M yet for instruction-following benchmarks — MMLU loses ~2.1 points and HumanEval loses ~3.8 points moving from Q4 to FP4 on the same Llama 3.3 70B base. If you care about top-of-class quality, run Q4 on a 4090. If you care about raw tok/s and accept a modest quality drop, the 5080 Super 24GB is the only card in this bracket that even offers the option.

<strong>Check RTX 5080 Super 24GB prices on Amazon →</strong> _Prices and availability current at time of publication._

<strong>See full RTX 5080 Super 24GB details →</strong>

🧪 Budget Pick: NVIDIA GeForce RTX 3090 Ti (used)

Spec chips: 24 GB GDDR6X · 1008 GB/s memory bandwidth · 10,752 CUDA cores · 450 W TGP · GA102 (Samsung 8nm) · CUDA 12.6+

✅ Pros

• Same 1008 GB/s bandwidth as a 4090 (matching Ampere's bandwidth to AD102's exactly), at $650–$850 used
• ~10% faster than a 3090 on memory-bound workloads (q4_K_M dense decode), often available cheaper than a 3090 because of the 450 W stigma
• Identical software profile to the 3090 — every Ampere kernel works, no new compatibility hunts
• Surprising sleeper buy in 2026: most enthusiasts skipped it at launch because the 4090 dropped 6 months later, so used supply is healthy

❌ Cons

• 450 W TGP, with no architectural efficiency advantage — runs hotter and louder than a 3090 for ~10% more throughput
• Demands a 1000 W PSU and the same 12VHPWR-adjacent connector concerns as the 4090
• Still no FP8 — same vLLM batching ceiling as the 3090
• Used-market ASIN supply is thinner than the 3090; expect more legwork to find a clean Founders Edition

The 3090 Ti's pitch is straightforward: it's a 3090 with the bandwidth of a 4090, at the price of a 3090. For purely memory-bandwidth-bound LLM decode, it's the best dollar-per-bandwidth card on the market in 2026. cited sources record 51 tok/s on Qwen 2.5 32B q4_K_M, a healthy 9% jump over the 3090 at no additional ecosystem cost.

The case against the 3090 Ti is the same as the case against the 4090: power draw. 450 W under sustained inference load adds up to real money on the electric bill if you're running 24/7. At an average 200 W load over 24 hours and $0.18/kWh (US national average, 2026 Q1), that's ~$315/year just to keep the card warm. A 3090 at the same workload averages closer to 165 W and costs ~$260/year. Numbers like that don't matter for occasional users; they add up if your card is doing batch inference overnight every night.

<strong>Check RTX 3090 Ti prices on Amazon →</strong> _Prices and availability current at time of publication._

<strong>See full RTX 3090 Ti details →</strong>

What to look for in a 24 GB GPU for local LLM

VRAM bandwidth (decode is bandwidth-bound)

LLM decode (the token-by-token generation step that dominates a chat session) is memory-bandwidth-bound, not compute-bound, on essentially every GPU in this bracket. Tokens-per-second on a quantized model scales almost linearly with GB/s — the 4090's 1008 GB/s and the 5080 Super 24GB's 1100 GB/s are why those two cards top our charts; the 7900 XTX's 960 GB/s is competitive on raw bandwidth but loses ~8% to kernel-quality gaps. Prompt processing (the encode step that builds the KV cache before generation starts) is compute-bound, but it's a fraction of the total wall-clock time for normal interactive use.

FP8 / FP4 tensor-core support

NVIDIA Ada (RTX 4090) added FP8; NVIDIA Blackwell (RTX 5080 Super 24GB, RTX 5090) added FP4. AMD RDNA 3 (7900 XTX) added FP8 in ROCm 6.2 with kernel-tuning still in progress. Older Ampere cards (3090, 3090 Ti) have no FP8 path at all — vLLM's FP8 KV cache mode silently falls back to FP16 on Ampere, costing you ~30% of your batched throughput. If you plan to host multiple users or run agentic loops, this matters a lot. If you only run single-user chat, it doesn't matter at all.

Power and cooling headroom

A 350 W card and a 450 W card draw genuinely different amounts of power and produce genuinely different amounts of heat under sustained inference. Inference loads are not burst loads like gaming — they're long, flat, continuous. Plan for a PSU rated at least 1.5× the GPU's TGP (so 800 W minimum for a 3090, 1000 W for a 4090 or 3090 Ti, 850 W for a 5080 Super) and a case with at least three intake fans plus dedicated rear/top exhaust. The thermal failure mode for an undercooled 3090 doing 24/7 inference is VRAM module thermal throttling, which silently knocks 15–20% off your tok/s without surfacing a single error.

Software ecosystem fit

The CUDA stack is still ~12 months ahead of ROCm on day-of-release model support. If you intend to chase new model releases the week they drop, buy NVIDIA. If your workload is pinned to a small set of well-supported models (Llama, Qwen, Mistral, DeepSeek mainline) and you can wait 2–6 weeks for ROCm kernels, AMD is genuinely competitive in 2026 — much more so than it was in 2024.

Multi-GPU scaling (NVLink is dead, PCIe matters)

NVLink is gone on consumer cards from the 4090 forward — the bridge connector and the protocol both. Multi-GPU LLM inference in 2026 means PCIe peer-to-peer over the motherboard, which is bandwidth-limited to PCIe 4.0 x16 (~32 GB/s) or PCIe 5.0 x16 (~64 GB/s) per direction. For tensor parallelism (splitting a single model across two GPUs), this is workable but adds latency. For pipeline parallelism (different models on different cards), it's irrelevant. If you anticipate ever running a >70 GB model across two 24 GB cards, prefer a card with PCIe 5.0 (5080 Super 24GB) or be prepared to live with PCIe 4.0's ~50% inter-GPU latency overhead.

FAQ

Is 24 GB enough for 70B models?

Yes — at q3_K_M or smaller. Llama 3.3 70B at q3_K_M weights in at ~20.4 GB; you have ~3.6 GB left for KV cache, which is enough for 4–8K context. At q4_K_M (the more common quality target) the same 70B model is 24.6 GB and won't fit on a single 24 GB card without offload, which collapses tok/s by 4–6×. If you want 70B at q4_K_M without offload, you need the 32 GB RTX 5090 or two cards in tensor parallelism. For 32B-class models (Qwen 2.5 32B, Yi 34B), 24 GB is comfortable at q4_K_M with room for 16K+ context.

Do I need an RTX card or will Radeon work?

Both work. The 7900 XTX runs llama.cpp, Ollama, vLLM, and SGLang on ROCm 6.2+ with within-spitting-distance performance of equivalent NVIDIA Ampere cards. The catch is release-day support for new models and the long tail of HuggingFace projects, both of which assume CUDA. If your workload is mainline (Llama / Qwen / Mistral / DeepSeek with no custom kernels) and you can wait a few weeks on new model launches, AMD is fine. Otherwise NVIDIA is the safer choice.

How much faster is FP8 vs FP16 in practice?

For single-stream decode, FP8 is ~5–15% faster than FP16 on the same card — meaningful but not transformative. For batched serving with vLLM, FP8 is 2–3× faster because the KV cache compresses 2× and you can fit double the concurrent requests in the same VRAM. If you're a solo user running interactive chat, FP8 doesn't change your life. If you're hosting multiple users or running an agent rig with high parallelism, FP8 is the difference between "this is usable" and "this is an actual production endpoint."

Used 3090 vs new 7900 XTX in 2026?

For maximum tok/s per dollar on solo CUDA workloads: used 3090. For new-warranty card with no thermal-pad-rot risk and a competitive ROCm stack: 7900 XTX. The 3090 wins on raw performance per dollar by ~15–25% depending on the model; the 7900 XTX wins on warranty, lower power draw, and not having to vet a used card's history. Most buyers should pick based on whether they trust the used market in their region — it's a quality-of-purchase decision more than a performance decision.

Will the 5080 Super 24GB age better than the 4090?

Probably yes, on a 3+ year horizon. FP4 is the future — every major model release in 2026 ships an FP4 quantization variant alongside Q4_K_M, and the quality gap is closing as quantization-aware training matures. The 5080 Super 24GB is the only 24 GB card with native FP4 tensor cores, so it gets faster on each new model release while the 4090 stays put. The catch is the next 12 months: FP4 kernel quality across vLLM, llama.cpp, and SGLang is still maturing, and the 4090 wins today on pure tok/s for most workloads. Buy the 5080 Super if you're optimizing for 2027–2028; buy the 4090 if you want maximum throughput now.

Sources

1. TechPowerUp RTX 4090 review — base raster, memory bandwidth, FP8 throughput numbers
2. TechPowerUp RX 7900 XTX review — Navi 31 architecture, ROCm baseline performance
3. Tom's Hardware GPU hierarchy 2026 — used-market pricing baseline for 3090 / 4090 / 7900 XTX
4. r/LocalLLaMA tok/s threads (reddit.com/r/LocalLLaMA) — community-aggregated tok/s for 3090, 4090, and 7900 XTX on Llama 3.3, Qwen 2.5, and DeepSeek V4 Pro
5. Phoronix ROCm 6.2 benchmarks — ROCm vs CUDA llama.cpp parity measurements on RDNA 3
6. NVIDIA Blackwell FP4 whitepaper, January 2026 (developer.nvidia.com) — E2M1 tensor-core specifications and sparsity caveats
7. Steam Hardware Survey, April 2026 — install-base data for the 3090 / 4090 (still the dominant >$1000 GPUs in the field)

Related guides

• Best 16GB GPU for Local LLM 2026
• Best 8GB GPU for Local LLM 2026
• Best CPU for Local AI 2026
• Used RTX 3090 Buying Guide: what to check before you pay

— SpecPicks Editorial · Last verified 2026-05-02