Guide · 2026-08-07

GPU Price Watch for Local LLM — June 2026

Last updated 2026-08-07

Street prices, VRAM per dollar, and the three cards actually worth buying this month for local inference.

By Mohamed Meguedmi · 8 min read

Prices in this watch are US street prices observed the week of June 20, 2026, cross-checked against retail listings and used-market medians. They move weekly — treat them as a snapshot, not a contract. If you want the live figures behind these tables, they are available through our public API (CC BY 4.0) and the open-source BestLLMfor MCP server, so you can pull current VRAM-per-dollar numbers straight into your own tooling.

The June 2026 Price Snapshot

Here is the full board, cheapest VRAM first. The $/GB column is the one that should drive most buying decisions for local inference — it tells you how much you pay for the single resource that decides whether a model runs at all.

GPU	VRAM	Bandwidth	Street price (USD)	$/GB VRAM	Sensible target
RTX 3060 12GB	12 GB	360 GB/s	$300	$25	7B–13B at Q4
RTX 3070	8 GB	448 GB/s	$212	$27	7B (tight)
RTX 3080	10 GB	760 GB/s	$322	$32	7B, fast
RTX 4070 Ti	12 GB	504 GB/s	$497	$41	13B at Q4
RTX 3090 (used)	24 GB	936 GB/s	~$750	$31	32B at Q4
RX 7900 XTX	24 GB	960 GB/s	$899	$37	32B at Q4
RTX 5080	16 GB	960 GB/s	~$1,450	$91	13B–32B (tight)
RTX 4090 (used, discontinued)	24 GB	1008 GB/s	~$2,755	$115	32B fast / 70B (paired)
RTX 5090	32 GB	1792 GB/s	$3,600–$4,300	~$122	70B at Q4, single card

Two things jump out. First, the entire bottom third of the table — the 3090, 7900 XTX, and 3080 — clusters around $30/GB, and everything above it more than triples that. Second, the 5090's headline bandwidth of 1,792 GB/s is genuinely in a different class, but you pay for it four times over per gigabyte.

VRAM Is the Only Spec That Matters First

The single most common and most expensive mistake is buying for raw speed. A model either fits in VRAM or it does not. When it does not, layers spill to system RAM over PCIe and throughput collapses by an order of magnitude — the fast card you paid a premium for now crawls.

Concretely, at Q4_K_M quantization the rough VRAM budgets are: a 7B model needs ~5–6 GB, a 13B needs ~9–10 GB, a 32B needs ~20–22 GB, and a 70B needs ~42–48 GB. Add 1–3 GB for context (KV cache) on top, scaling with sequence length. That is why 24 GB is the magic number in June 2026: it is the smallest amount that holds a full 32B model — such as Qwen3-Coder 32B Q4_K_M — entirely on-card with room for a useful context window. You can confirm the file sizes yourself on the Qwen model cards and pull ready-made quants from the Ollama library.

Only after a model fits does bandwidth take over, because token generation is memory-bound: tokens-per-second scales almost linearly with GB/s once the weights are resident. That is the whole reason the 3090 and 4090 remain relevant years after launch — 900+ GB/s of bandwidth ages far better than compute does for inference. For the underlying reason, the llama.cpp project documents how quantized inference is bottlenecked on memory throughput, not FLOPs.

Best Value by Budget Tier

Match your budget to the largest model you actually intend to run. Buying more card than your target model needs is wasted money; buying less means the model never fits.

Budget	Pick	Largest comfortable model	Why
Under $350	RTX 3060 12GB	13B Q4	Cheapest 12 GB card; the true entry point for local LLMs.
$700–$900	Used RTX 3090	32B Q4	Best VRAM-per-dollar on the market. The default recommendation.
$900 (new only)	RX 7900 XTX	32B Q4	Best new 24 GB card if you refuse used and accept ROCm.
$1,400–$1,600	2× used RTX 3090	70B Q4	Two 3090s beat one 5090 on price and match it on total VRAM.
$3,600+	RTX 5090	70B Q4, single card	Only pick if you need single-card simplicity or 1,792 GB/s.

The pattern is clear: two used 3090s (48 GB total for ~$1,500) undercut a single 5090 (32 GB for ~$3,900) while giving you more aggregate VRAM. The 5090 wins only on power draw, PCIe slot count, and the convenience of not managing a multi-GPU split — real advantages, but ones most individuals do not need. You can model the trade against your token volume in our cost calculator.

NVIDIA vs AMD in June 2026

AMD's RX 7900 XTX at $899 is, on paper, the best new-card deal here: 24 GB and 960 GB/s for less than half a new 4090. ROCm 7.2 has genuinely closed much of the usability gap, and llama.cpp plus vLLM both run well on it today.

The honest verdict: if you want zero-friction tooling, buy NVIDIA. If you want the most VRAM per new dollar and will spend an afternoon on ROCm setup, the 7900 XTX is the smart contrarian buy.

CUDA is still the default target for nearly every quantization tool, fine-tuning script, and community how-to. That ecosystem lead is why a used 3090 at ~$750 often beats a new 7900 XTX at $899 for most readers: it is cheaper, has near-identical bandwidth, and never asks you to debug a runtime. AMD earns its place for buyers who specifically want a warranty and a new card. See our full model-by-hardware pairings in the catalog.

What's Moving the Market This Month

Three forces explain every number in the snapshot table:

• 5090 scarcity. Demand from AI buyers keeps the 5090 at $3,600–$4,300 against a $1,999 MSRP — an 80–115% premium that shows no sign of normalizing while 32 GB single cards stay rare.
• 4090 discontinuation. With production ended, used 4090s have inverted the normal depreciation curve and now trade near $2,755 and climbing. It is now a collector-priced card, not a value one.
• 3090 supply. A steady stream of used 3090s from gaming upgrades keeps the 24 GB tier liquid and cheap. This is the single most important fact for budget-conscious local LLM buyers in mid-2026.

The takeaway: the sweet spot is not moving up-market. If anything, the value case for last-generation 24 GB silicon has strengthened as new-card prices detach from MSRP.

How to Match a GPU to Your Model

1. Pick the model and quant first. Decide on, say, Qwen3-Coder 32B at Q4_K_M or Llama-3.3 70B at Q4 before looking at hardware.
2. Look up the file size. Check the model card on Hugging Face or the Ollama library page for the exact GGUF size.
3. Add a context margin. Budget an extra 1–3 GB for the KV cache, more for long contexts (32K+).
4. Choose the cheapest card that clears that total. Use the $/GB column above — do not overbuy.
5. Only then compare bandwidth between the cards that fit, to estimate tokens per second.

Follow that order and you will never make Elias's mistake — returning a fast card with too little memory and settling for a used 3090 that runs the 32B model he wanted at full speed. Our benchmark hub lists measured tokens-per-second per card so you can size expectations before you buy.

Verdict

The market has not changed the answer it has been giving all year: buy 24 GB, buy used, buy a 3090. New silicon is detaching from MSRP while last-gen cards stay cheap and plentiful, which only strengthens the value case for the mid-tier.

If you want…	Buy	Approx. cost
The cheapest way to run any local LLM	RTX 3060 12GB	$300
The best overall value (our pick)	Used RTX 3090	~$750
Best new 24 GB card per dollar	RX 7900 XTX	$899
70B on the cheapest hardware	2× used RTX 3090	~$1,500
Single-card 70B, no compromises	RTX 5090	$3,600–$4,300

Prices will move again next month; the framework will not. Fit the model in VRAM first, then chase bandwidth, and ignore the halo cards unless single-slot simplicity is worth a 4× premium to you. For live figures, our rankings and public data feeds stay current between editions of this watch.