Are used RTX 3090s still the best tokens-per-dollar for LLM inference in 2026?

For raw decode speed on quantized models, yes — a pair of used 3090s (~$1,100-1,400 secondhand) still delivers 25-35 tok/s on 70B Q4 and runs Qwen/Llama 235B-A32B MoE models faster than any unified-memory box at triple the price, because 936 GB/s of combined GDDR6X bandwidth beats every deskbox. The trade-offs are 48GB total ceiling, 700-900W under load, and aging cards with no warranty.

Mac Studio or Ryzen AI Max 395 for running LLMs locally?

The Mac Studio M4 Ultra (512-816GB unified, ~800 GB/s) is the memory king — the only deskbox that runs frontier-density models like Llama 235B FP8 or Qwen 235B at FP8 comfortably. The Ryzen AI Max 395 (128GB, ~256 GB/s, $1,700-2,000) wins on price-per-GB of local memory and integrates with your Windows/Linux toolchain via LM Studio or Ollama. If you need maximum capacity, Mac; maximum value in a quiet all-in-one, Ryzen.

How much VRAM or unified memory do you need to run a 70B model locally in 2026?

For a dense 70B at Q4 (GGUF or AWQ), budget roughly 40-45GB — model weights plus KV cache for a usable context. That means a pair of 24GB GPUs, a 48GB+ unified-memory box, or the Mac/Ryzen/Spark class. If you want 70B at FP8 with long context, only the Mac Studio Ultra and multi-GPU desktops clear the bar, and a dual-3090 rig does it only at Q4.

Is local LLM inference actually cheaper than cloud API in 2026?

For sustained, high-volume workloads — coding assistants running all day, batch document processing, privacy-bound data — yes, local wins within months. A dual-3090 build (~$1,400) producing 60-100k tokens per working day replaces roughly $40-80/month of comparable API spend, paying back inside 18-24 months while keeping data on-prem. For intermittent use, the cloud API is still cheaper and smarter.

Local LLM Hardware in 2026: The 4 Tiers That Actually Make Sense (DGX Spark, Ryzen AI Max, Mac Studio, Used 3090s)

Q: Is the NVIDIA DGX Spark good value for local LLM inference in 2026?

It depends what you value. The Spark (GB10 Grace Blackwell, 128GB LPDDR5x unified, ~273 GB/s) delivers unmatched memory capacity in a deskbox and runs the full NVIDIA stack — TensorRT-LLM, NIM, FP4. But at roughly $4,000 for ~15-20 tok/s on 70B-class models, its tokens-per-dollar is the worst of the four tiers. You are paying for the ecosystem and the 200B-class model headroom, not speed.

Affiliate disclosure: Some links in this guide earn us a commission at no extra cost to you. We only recommend hardware we'd run ourselves. Read our full disclosure.

The 2026 landscape: four tiers, four philosophies

Three years into the local-AI boom, the hardware market has finally stopped pretending there's one answer. The 2026 local LLM stack splits into four distinct tiers, each built around a different bet on what matters:

NVIDIA DGX Spark ($4,000 street) — betting you'll pay for the CUDA ecosystem and the memory ceiling.
Apple Mac Studio M4 Ultra ($4,000-7,500) — betting on absurd unified-memory capacity at real bandwidth.
AMD Ryzen AI Max 395 mini-PCs ($1,700-2,100) — betting on price-per-GB and silence.
Used RTX 3090 desktops (~$1,100-1,500) — betting that raw GDDR6X bandwidth still beats everything per dollar.

The split matters because inference — unlike training — is a memory-bandwidth and memory-capacity problem, not a FLOPS problem. Decode speed for a single stream is essentially (memory bandwidth) ÷ (model size). A $200 GPU with perfect bandwidth beats a $40,000 accelerator with terrible bandwidth for one user chatting with a 70B model. That's why this market looks so strange from the outside: the most expensive box is often not the fastest at the task most people actually do.

Tier 1: NVIDIA DGX Spark — the ecosystem tax

NVIDIA's DGX Spark is the company's first consumer-priced entry in years, and it's a statement product: GB10 Grace Blackwell SoC, 128GB of LPDDR5x unified memory, ~273 GB/s of bandwidth, NVLink-style coherence between CPU and GPU, and the full DGX software stack in a box the size of a hardback book. For developers who want to prototype with the exact APIs (TensorRT-LLM, NIM microservices, FP4 quantization) they'll later deploy against data-center DGX hardware, it's unmatched. Nothing else in this tier runs the official NVIDIA local stack end to end.

The honest critique: tokens-per-dollar is the worst of the four tiers. 273 GB/s is less than a used RTX 3090's 936 GB/s, and it shows in decode speed — practical community numbers land around 15-20 tok/s on 70B-class Q4 models, with MoE models like Qwen/Llama 235B-A32B faring better. You're paying roughly $4,000 for capacity (128GB in one quiet box) plus ecosystem purity, not for speed. If your workflow is "tinker with the model that will also run on my company's DGX cluster," the premium buys real compatibility. If your workflow is "answer my prompts quickly," it doesn't.

Tier 2: Mac Studio M4 Ultra — the memory king

Apple's M4 Ultra Studio remains the only deskbox that runs frontier-density models at full precision. With 512-816GB of unified memory at ~800 GB/s, it can host Llama-235B or Qwen3-235B at FP8 with tens of thousands of tokens of context — something no consumer GPU rig and neither the Spark nor the Ryzen can touch. MLX and llama.cpp/Kobold's Metal backends are mature, and community benchmarks show the M4 Ultra delivering 40-60 tok/s on dense 70B Q4 and usable throughput on 100B+ MoE models.

The two caveats: price, and Apple's API surface. A loaded Studio is $5,000-8,000 — more than any DIY tier here — and if your production stack is CUDA or ROCm (Windows/Linux), you're running Apple's stack at the edge and exporting model formats across a boundary. For researchers, writers, and professionals who want the biggest models that fit on a desk, nothing else competes. For tinkerers who want to train LoRAs or run the exact stack their servers run, it's the wrong tool.

Tier 3: Ryzen AI Max 395 — the value deskbox

The AMD Ryzen AI Max 395 mini-PC class (Framework Desktop, GMKtec EVO-X2, HP G2a, Beelink GTR9 Pro — all shipping through 2025-2026) is the tier nobody expected to matter and everybody underprices. 128GB unified LPDDR5x at ~256 GB/s for $1,700-2,100 is half the Spark's price for comparable capacity — and the ecosystem is the standard Windows/Linux toolchain: LM Studio, Ollama, llama.cpp, vLLM on ROCm. No special APIs, no model-conversion gymnastics.

Performance lands where the bandwidth says it should: roughly 8-12 tok/s on dense 70B Q4, and genuinely good numbers on MoE models (Qwen3-235B-A32B around 20-30 tok/s, since only ~32B parameters are active per token). The tier's real argument is price-per-GB of local memory: ~$13-16 per usable GB versus the Spark's ~$31 and the Mac Studio's $6-10 at the very high end. If you want to run 70B-class or MoE models locally without a server rack, this is the default 2026 answer — quiet, low-power (140W total), and sitting on your existing desk.

The Ryzen AI Max 395 class runs 70B and 235B-A32B MoE models locally in 128GB of unified memory — no server rack required:

Ryzen AI Max 395 mini-PCs on Amazon →

Tier 4: Used RTX 3090s — the bandwidth bargains

And then there's the answer nobody wants to be true: a pair of used RTX 3090s still wins outright on tokens-per-dollar in 2026. At $500-700 each on the secondhand market, two cards give you 48GB of GDDR6X and a combined ~936 GB/s — bandwidth that no unified-memory deskbox under $5,000 can touch. Practical numbers on 70B Q4 with llama.cpp or exllamav2: 25-35 tok/s, and MoE models (the 235B-A32B class) absolutely fly at 60+ tok/s because they fit at Q4 and only activate ~32B parameters per token.

The costs are real, and they're why this tier isn't for everyone: 700-900W under load (your power bill notices), a machine that sounds like a jet during long batch jobs, 48GB hard ceiling, and cards with no warranty and mining-era histories. It's also the only tier that requires a real desktop chassis, a real PCIe topology (preferably on a board that gives both cards full x8/x8), and the willingness to babysit thermals. But per dollar of throughput and per GB of usable memory at real bandwidth, nothing in 2026 beats the secondhand Ampere market — our CPU-for-AI guide covers the pairing story for a Ryzen 7950X-class host.

The used-3090 route is still the tokens-per-dollar king in 2026 — power supply and case matter as much as the cards:

RTX 3090-class GPUs on Amazon →

The math that actually matters: tokens per dollar

Here's the comparison nobody in vendor marketing wants in one table — decode throughput on a 70B-class Q4 model, divided by purchase price. MoE numbers are for the 235B-A32B class (the biggest thing each box can hold):

System	Price	Memory	70B Q4	MoE 235B	The trade-off
DGX Spark	~$4,000	128GB @ 273 GB/s	~15-20 tok/s	~15-25 tok/s	Ecosystem purity, slow decode
Mac Studio M4 Ultra	$4,000-7,500	512-816GB @ ~800 GB/s	~40-60 tok/s	~30-45 tok/s	Capacity king, Apple-only stack
Ryzen AI Max 395	$1,700-2,100	128GB @ ~256 GB/s	~8-12 tok/s	~20-30 tok/s	Best price-per-GB, modest bandwidth
2× Used RTX 3090	~$1,100-1,500	48GB @ 936 GB/s	~25-35 tok/s	~60+ tok/s	Fastest per dollar, loud, 48GB ceiling

Numbers are representative community benchmarks, not lab-grade measurements — quantization, context length and backend drift move each figure ±20%. The rankings have been stable all year.

Two structural notes the table hides. First, MoE models changed the deskbox math: the 235B-A32B class squeezes frontier-ish quality into ~32B active parameters, which means the Ryzen AI Max — modest on dense models — delivers genuinely usable speed on the biggest open models. Second, the used-Ampere market is a depleting resource: every year of mining-era attrition and AI demand tightens supply, and there is no 2026 replacement for $650-of-bandwidth. The DDR5 market chaos we've been tracking all autumn has pushed new-build RAM prices up at the same time, which only strengthens the secondhand-GPU argument for budget builders.

Which tier for which person

Developer targeting NVIDIA's stack end-to-end (TensorRT-LLM → DGX cloud): DGX Spark. The premium buys compatibility, and 128GB in a deskbox is real. Accept 15-20 tok/s on dense models.
Researcher/professional who needs the biggest open models at full precision: Mac Studio M4 Ultra. 512-816GB runs the models nothing else on a desk can, at 40-60 tok/s on 70B. Bring the Apple-stack tolerance.
Everyone's default 2026 local-AI purchase: Ryzen AI Max 395 mini-PC. Half the Spark's price, same 128GB, standard toolchain, silent. MoE models make it genuinely fast where it counts.
Maximum tokens per dollar, tolerate the noise: dual used RTX 3090. Fastest decode per dollar in the market, 48GB ceiling, 800W of honesty about what compute costs.

And if your actual workload is "chat with a 7B-14B model occasionally" — none of these. A 16GB consumer laptop GPU or a homelab box you already own runs that tier fine, and the cloud API is cheaper than any deskbox for intermittent use. The four tiers above exist for people running 70B-class and larger, daily.

FAQ

Is the NVIDIA DGX Spark good value for local LLM inference?

For ecosystem purity and 128GB capacity, yes; for raw speed, no — ~15-20 tok/s on dense 70B Q4 is the slowest of the four tiers per dollar. Full breakdown in the FAQ schema above.

Are used RTX 3090s still the best tokens-per-dollar?

Yes — 936 GB/s of combined bandwidth at ~$1,400 total is unbeatable in 2026, if you accept 48GB ceiling, 700-900W draw and no warranty.

Mac Studio or Ryzen AI Max 395?

Mac for maximum model size at full precision (up to 816GB); Ryzen for best price-per-GB with the standard Windows/Linux toolchain at $1,700-2,100.

How much memory for a 70B model locally?

~40-45GB at Q4 including KV cache — two 24GB GPUs, or any 128GB unified-memory box in this piece.

Is local inference cheaper than a cloud API?

For sustained daily volume (60-100k tokens/workday), a dual-3090 build pays for itself in 18-24 months and keeps data on-prem. For intermittent use, the API is still cheaper.