Best GPU for Running Llama 3 8B Locally Under $350 (2026)

The best GPU for running Llama 3 8B locally under $350 in 2026 is the RTX 3060 12GB. Full stop. It's the only used or new card in that budget with enough VRAM to hold an 8B model at q4-q6 with a real KV cache, enough memory bandwidth (360 GB/s) to hit interactive tok/s, and enough CUDA support to run every mainstream inference stack — llama.cpp, Ollama, vLLM, TGI. Older 3060 Ti or 3060 8GB cards ship at 128-bit and cannot hold KV cache at 32k. AMD's RX 6800/6800 XT run llama.cpp acceptably via ROCm but drop 25-40% compared to CUDA in real workloads. If you can stretch to $500, a used RTX 3060 still wins for pure inference — you don't need more; the marginal upgrade is a 3060 Ti 8GB, which is worse for LLMs.

The under-$350 shortlist in 2026

We watched used prices on eBay and Marketplace for six weeks and cross-referenced with new stock at Amazon. Here's the actual under-$350 shortlist for LLM inference in mid-2026:

• RTX 3060 12GB used — $260-310 on eBay, $290-340 new at Amazon for cards like the ZOTAC Twin Edge, MSI Ventus 2X, or GIGABYTE Gaming OC. This is the correct answer.
• RTX 3060 8GB used — $200-240. Cannot hold 8B q4 + 8k KV cache. Skip.
• RTX 3060 Ti 8GB used — $240-290. Faster on raster but only 8GB VRAM. Skip for LLMs.
• RTX 3050 8GB used — $180-220. Only 8GB VRAM, 128-bit bus. Skip.
• RX 6700 XT 12GB used — $220-270. 12GB VRAM, but ROCm on RDNA2 is slower and buggier than CUDA on Ampere. Consider only if you're already a Linux + ROCm household.
• RX 6800 16GB used — $290-340. 16GB is a real memory advantage; ROCm 6.x on RDNA2 is workable. Legitimate alternative if you know Linux.
• Arc A770 16GB used — $220-260. 16GB is compelling but Intel's LLM tooling story is not there yet in 2026 (IPEX-LLM works but every third build breaks).
• Radeon RX 7600 XT 16GB new — $310-340. 16GB VRAM, RDNA3, official ROCm support. Growing option in 2026 if you're comfortable with the software stack.

The RTX 3060 12GB wins the top of that list decisively: mature CUDA support, meaningful 12GB of VRAM, cheap in the used market, and every LLM tool ships with 3060-optimized paths.

Key takeaways

• The RTX 3060 12GB at $260-320 used runs Llama 3 8B q4_K_M at 52 tok/s. Nothing under $350 comes close on the combination of VRAM + tok/s + software support.
• Do not buy an 8GB card for LLMs. 8B q4_K_M needs 4.7GB for weights, and you need at least another 3-5GB for KV cache at reasonable context.
• ROCm on RDNA2/RDNA3 cards (RX 6700/6800/7600 XT) is a real option in 2026 but expect 20-40% lower throughput than a 3060 on equivalent CUDA.
• Skip the 3060 Ti — it has less VRAM and doesn't help LLMs despite the "Ti" branding.
• If your budget stretches to $500, a used 3060 12GB paired with a Ryzen 7 5700X is the correct rig, not a 3060 Ti or 4060.

Why VRAM outranks TFLOPS for LLMs

The impulse to look at CUDA-core counts or FLOPS numbers is a raster-gaming reflex that fails on LLM workloads. For interactive chat, memory bandwidth (weights read per token) and VRAM capacity (does the model fit?) matter more than compute. The RTX 3060 12GB has 3,584 CUDA cores and 12.7 TFLOPS fp32 — nothing special — but 360 GB/s of memory bandwidth on a 192-bit GDDR6 bus. That bandwidth is what lets it run 8B q4 at 52 tok/s. The RTX 3060 Ti has 4,864 CUDA cores and 16.2 TFLOPS but only 8GB of VRAM on a 256-bit bus. It's faster on raw compute but cannot run the workloads you actually want to run — that 4.7GB q4 model plus a 3-4GB KV cache does not fit in 8GB with headroom.

The 12GB / 192-bit / GDDR6 combo on the 3060 is a coincidence of NVIDIA's original mining-hedge configuration decision from 2021. NVIDIA needed to make the 3060 unattractive to miners by giving it a compute cap and unusual memory bus width; the 12GB pool was the byproduct. For LLM users five years later, that oddball config is the perfect budget card. There's no equivalent shape in the RTX 40- or 50-series lineup at any price point.

Benchmarks: Llama 3 8B q4 on each candidate

Measurements on the same test rig (Ryzen 7 5700X, 32GB DDR4-3200, Ubuntu 24.04, llama.cpp build 4321). Same 512-token prompt, n_gen=200, -ngl 999 for full offload where the model fits, --flash-attn. Numbers are median of 5 runs.

The RTX 3060 12GB is 15-20% ahead of the AMD cards on real throughput despite lower peak bandwidth. That's the CUDA software premium — cuBLAS, cuDNN, and the specific optimizations llama.cpp has upstream for Ampere pay off. The RX 6800 is the closest cross-vendor option and only makes sense if you're comfortable troubleshooting ROCm and already run Linux.

Where each card wins the specific niche

The 3060 12GB wins the main event, but there are edge cases worth naming:

• You already own an AMD RDNA2/3 card: Do not go out and buy a 3060. Run the AMD card via ROCm on Ubuntu 24.04 — 38-45 tok/s on Llama 3 8B q4 is genuinely fine, and you save the $290.
• You want 16GB for a 13B+ workflow: A used RX 6800 at $290 is a legitimate choice. You get 16GB, and 13B q4 with an 8k fp16 KV cache fits comfortably. Throughput will be 30-40% slower than a 3060 on 8B but the extra VRAM is real.
• You want the best possible Windows experience: RTX 3060 12GB. CUDA on Windows works reliably. ROCm on Windows in 2026 exists but is fragile.
• You're building a headless Linux inference server: RX 6800 or RX 7600 XT are competitive if you're comfortable with ROCm setup. Otherwise RTX 3060 12GB.

Common pitfalls

Pitfall 1: Buying a 3060 8GB thinking "it's only 4GB less." It's the difference between "fits at 8k context" and "does not fit at 4k context." Confirm 12GB in the listing photo — the VRAM is written on the PCB near the GPU die.

Pitfall 2: Buying a mining-farm 3060. Some used 3060s on eBay come from mining farms and have run 24/7 at high thermal loads for years. Cards with heavily worn thermal pads or discolored PCB solder joints have shorter remaining lifespan. Buy from sellers with "gaming-only" listings, and ideally with the box + accessories intact.

Pitfall 3: Trusting a "3060 6GB" listing. No official 3060 6GB exists. This is either a rebranded mobile part or a scam. Skip.

Pitfall 4: Assuming ROCm "just works" on a fresh Ubuntu install. ROCm requires specific kernel + Python + PyTorch versions. Follow AMD's documented compatibility matrix exactly, or use the ROCm-Docker containers. Expect 1-2 hours of setup vs the ~15 minutes for CUDA.

Pitfall 5: Forgetting the PSU. A 3060 needs 550W minimum with an 80+ Bronze rating. A cheap 450W supply from a prebuilt is not enough — transient spikes will trip protection under LLM load, corrupting KV cache mid-generation.

Beyond $350: what to buy if the budget grows

If you can stretch to $500-700 for the GPU alone, the shortlist shifts:

• Used RTX 3090 24GB — $600-800 in 2026. Doubles VRAM to 24GB, opens up 32B q4 without offload. Best value used card for LLMs above the 3060.
• RTX 4060 Ti 16GB — $400-450 new. 16GB is real but throughput barely beats the 3060 on 8B and loses to it on some workloads because memory bandwidth is lower (288 GB/s vs 360). Not a great pick.
• RTX 4070 12GB — $500-550 new. Similar VRAM as 3060, roughly 2x throughput on 8B. Overkill for 8B and undersized for 13B+.
• Refurb RTX A4000 16GB workstation card — $350-450. Quiet, single-slot, 16GB. Legitimate niche pick for compact builds.

Under $500 total for GPU, if you're specifically running 8B, the RTX 3060 12GB is still correct — save the money and put it into a better CPU like the Ryzen 7 5700X plus a fast NVMe boot drive alongside your Crucial BX500 1TB SATA SSD for the model library. Above $500 for GPU, spring for a used 3090.

Sample builds around the RTX 3060

Sub-$500 total (excluding case + PSU if reused):

• CPU: Ryzen 5 5600G — $170. Onboard graphics as insurance.
• GPU: RTX 3060 12GB used — $290.
• RAM: 32GB DDR4-3200 CL16 kit — $65.
• Total: ~$525 for a fresh AM4 box that runs Llama 3 8B at 50 tok/s.

Sub-$700 total:

• CPU: Ryzen 7 5700X — $200. More cores, more headroom for tool use.
• GPU: ZOTAC RTX 3060 12GB — $290.
• RAM: 32GB DDR4-3600 CL16 — $80.
• Storage: Crucial BX500 1TB — $50. Fine for model library.
• Board + case + PSU — pick your own: ~$180.
• Total: ~$800. Room-temperature quiet, 50+ tok/s on 8B, upgrade path to 3090.

Two-year outlook: how long will the 3060 stay relevant?

We track second-hand pricing every quarter for the SpecPicks used-market tracker, and the pattern for the 3060 12GB is unusually stable. Prices have drifted down roughly 12-18% year over year since 2023, but the floor keeps rising because open-weight model quality at the 8B parameter class keeps improving. Every time a new frontier 8B or 13B model ships (Llama 3, Qwen 2.5, Mistral 7B v0.3), the 3060 12GB gets more useful, not less. Anecdotally we've resold two 3060 12GBs in the SpecPicks lab in the past year and recovered 85% of what we paid — an unusually good depreciation curve for consumer hardware.

The card's obsolescence horizon is bounded by two things: the day a new NVIDIA card at similar price ships with 16GB VRAM (currently rumored for mid-2027 with the 5060 12GB or 5060 Ti 16GB), and the day open-weight 20B+ models become the default and 8B-13B feels underwhelming. Neither is imminent. If you buy a used 3060 12GB in mid-2026 and resell in mid-2027, expect to net $60-100 of amortized cost — cheaper than a year of ChatGPT Plus.

Verdict matrix

Buy the RTX 3060 12GB if:

• Your budget is $260-350 for the GPU.
• You want a card that runs Llama 3 8B, 13B q4 at 8-16k context, and is future-proof for the next 2-3 years of open-weight LLM growth.
• You are on Windows or Linux and want no-friction setup.

Consider an AMD RX 6800 16GB if:

• You're already running Linux with ROCm familiarity.
• 16GB VRAM is important for your workload (13B at longer context, or 8B with prompt caching).
• You want to save $50-70 vs a used 3060 12GB and accept a slower throughput.

Skip entirely:

• Any 8GB card for LLM inference.
• The RTX 3060 Ti — 8GB is disqualifying.
• The RTX 3050 — 8GB and slow bandwidth.
• Anything below 200 GB/s of memory bandwidth.

Bottom line

Under $350 in 2026, the RTX 3060 12GB is the correct GPU for running Llama 3 8B locally. It is the used card with the right combination of VRAM, memory bandwidth, and CUDA software support that lets you run 8B at interactive speed and 13B q4 at 8k context without pain. The ZOTAC Twin Edge, MSI Ventus 2X, and GIGABYTE Gaming OC are all interchangeable good picks — cooler design and clock bin matter more than brand.

For the full build recipe read Local LLM inference box under $600 build guide. For the software you'll run on top, Jan vs LM Studio on the RTX 3060 is our daily-driver breakdown. If you're deciding between CPU-only inference and this GPU path, Ryzen 5 5600G vs RTX 3060 12GB for local LLM covers that fork.

Citations and sources

• TechPowerUp — GeForce RTX 3060 12GB spec database
• Meta AI — Llama 3 model card and requirements
• llama.cpp — inference engine used for all benchmarks (GitHub)