RTX 4080 Super 16GB for Local AI in 2026: 736 GB/s on the Used Market, and Why the Math Is Tighter Than You'd Think

By RunAIHome Team · 13 min read
gpurtx-4080-superlocal-ailocal-llmhardwarebenchmark

TL;DR: The RTX 4080 Super’s 736 GB/s memory bandwidth delivers a genuine 56% speed boost over the RTX 5060 Ti 16GB on 14B models — but at $860 used versus $429 new, you’re paying $431 extra for that throughput. The real problem is the RTX 5070 Ti sitting $120 above it with 22% more bandwidth and lower power draw.

RTX 5060 Ti 16GBRTX 4080 Super (used)RTX 5070 Ti 16GB
Best forBudget 8B–14B inference14B model speed, used valueMaximum 16GB throughput
Price~$429 new~$860 used~$979 actual ($749 MSRP)
Bandwidth448 GB/s GDDR7736 GB/s GDDR6X896 GB/s GDDR7
Qwen3 14B @ 16K ctx32.9 tok/s~61 tok/s~75 tok/s (est.)
TDP180W320W300W
The catchBandwidth-limited on 14B+Used only, 2× the power drawSupply-constrained, over MSRP

Honest take: If a 5070 Ti at street price materializes near you, buy that instead. If you’re staring at a used 4080 Super at $830–$860 and the 5070 Ti is still $200 over MSRP in your region, the 4080 Super is a legitimate buy — not a compromise.

Why bandwidth is the only spec that moves the needle for LLM inference

Token generation is almost entirely a memory bandwidth problem. The GPU reads billions of model weights from VRAM every second to compute each new token. The faster it reads, the more tokens per second it produces. CUDA core count, shader speed, even FP8 support — none of those matter much during autoregressive generation. Bandwidth does.

That’s why the RTX 4080 Super’s 736 GB/s matters. Compare it to the tier below it:

  • RTX 5060 Ti 16GB: 448 GB/s — solid budget card, but 39% less bandwidth
  • RTX 5070 12GB: 672 GB/s — faster than the 5060 Ti, but 12GB cap rules out most 20B+ models
  • RTX 4080 Super 16GB: 736 GB/s — the used market’s 16GB bandwidth leader outside the 5070 Ti
  • RTX 5070 Ti 16GB: 896 GB/s — the new-generation benchmark

The 4080 Super’s GDDR6X bus runs at 22.4 Gbps over a 256-bit interface. By contrast, the 5060 Ti uses GDDR7 but on a narrower 128-bit bus — GDDR7’s per-pin speed is faster, but the bus width halves it. The 4080 Super’s wider bus wins.

What this means in practice: on a model like Qwen3 14B in Q4_K_M quantization, a larger fraction of the model fits in the attention layer’s KV cache bandwidth rather than spilling to slower paths. You get faster context reuse at longer windows.

Actual benchmark numbers

Rost Glukhov’s Ollama 0.17.7 benchmark suite (March 2026, RTX 4080 16GB) — the 4080 is 716 GB/s vs. the Super’s 736 GB/s, so the numbers are within ~3%:

  • Qwen3 14B at 19K context: 61.85 tok/s generation
  • Mistral Small 3 14B: 70.13 tok/s
  • GPT-OSS 20B (fully in VRAM): 82+ tok/s

For reference, modelfit.io reports the RTX 4080 Super headline speed at 79 tok/s on 14B parameter models at standard context lengths — consistent with the above.

The RTX 5060 Ti 16GB (hardware-corner.net, 2026 benchmark suite) gets 32.9 tok/s on Qwen3 14B at 16K context in Q4_K_M. That’s a real 56% throughput gap between the cards.

At shorter context windows, the 4080 Super advantage is even wider because the bandwidth bottleneck is less severe for the 5060 Ti at shorter contexts — but the 5060 Ti’s narrower bus still caps out sooner.

What this means for daily use

If your primary workflow is chatting with a 7B–9B model, the 5060 Ti 16GB is fast enough and the 4080 Super is overkill. The 5060 Ti runs Llama 3.1 8B at 71 tok/s — already faster than you can read. But once you move to 14B models as your daily driver (and in mid-2026, a Q4 Qwen3 14B is genuinely your best value local model), that 32.9 vs 61.85 tok/s gap becomes noticeable. Coding loops with Continue.dev, document chat with Open WebUI, or long-session agentic pipelines — all feel meaningfully different at double the token rate.

What the 4080 Super can actually run

With 16GB GDDR6X VRAM, the 4080 Super fits:

ModelQuantizationVRAM UsedSpeed
Llama 3.1 8BQ8_08.5 GB~95 tok/s
Qwen3 14BQ4_K_M9.4 GB~62 tok/s
Qwen3 14BQ6_K11.8 GB~55 tok/s
Mistral Small 3 22BQ4_K_M13.3 GB~41 tok/s
Llama 3.3 70BQ2_K28 GB❌ CPU offload
Llama 3.3 70BIQ1_S~14 GB~18 tok/s (heavy quality loss)

The 14B tier is the sweet spot. Q6 and Q8 quants of 14B models fit with headroom for long context, and you get good quality without Q4 rounding artifacts.

70B models in standard quantizations don’t fit — same situation as the 5060 Ti 16GB and 5070 Ti 16GB. If 70B is your target, you need either the RTX 3090 24GB or the Mac Studio M4 Max 128GB unified memory path. See our VRAM guide for Llama models for the full breakdown.

Mixture-of-Experts models run surprisingly well. The RTX 5060 Ti benchmark article showed the 5060 Ti handling Qwen3.5-35B-A3B at 44 tok/s. The 4080 Super should push that to roughly 65 tok/s based on the bandwidth ratio — making MoE models a genuine strength of this card.

Context window scaling

Long contexts cost bandwidth. At 32K context on Qwen3 14B Q4_K_M, the 5060 Ti 16GB drops from 32.9 tok/s to approximately 26 tok/s — a 21% slowdown. The 4080 Super degrades proportionally: from ~62 tok/s at 16K to approximately 50 tok/s at 32K context. Even degraded, it stays ahead of the 5060 Ti’s baseline speed.

For 128K context — if you’re running tools like llama.cpp with flash attention enabled — both cards will slow down significantly. The 4080 Super’s wider bus gives it more resilience here. Very long-context retrieval-augmented generation (RAG) pipelines where each call uses 50K+ tokens will see a larger benefit from the 4080 Super over the 5060 Ti than the headline benchmarks suggest.

Power draw: the 140W gap you’re paying for every month

The RTX 5060 Ti 16GB runs at 180W TDP. The RTX 4080 Super sits at 320W TDP. That’s 140W more under load — and it matters more than people account for.

Electricity cost at $0.12/kWh, 8 hours/day active use:

CardDailyMonthlyAnnual3-Year
RTX 5060 Ti 16GB (180W)$0.17$5.18$62.18$186.55
RTX 4080 Super (320W)$0.31$9.22$110.59$331.78
Difference$0.14$4.04$48.41$145.22

Over three years, the 4080 Super costs $145 more in electricity. Combined with the $431 hardware premium over the 5060 Ti, you’re looking at a $576 total cost difference over 36 months to get 56% more tok/s on 14B models.

If your time is worth anything, that math can close fast. A developer saving 10 minutes per day in coding assistant latency — 14B models instead of 8B, longer context without slowdown — over three years is 182 hours recaptured. At $50/hr freelance rate, that’s $9,100. The GPU math flips.

But that’s the rosy case. If you’re mostly chatting with 7B–8B models for fun, none of this pays off.

Used market reality: June 2026

Used RTX 4080 cards (non-Super) run approximately $795 on eBay as of June 2026, per bestvaluegpu.com tracking data. The Super variant commands a modest premium at around $860 — reflecting the slightly better specs.

Used GPU risks to price in:

  • No warranty: If the card dies, it’s fully out of pocket. Factor $50-100 into your effective cost.
  • Crypto mining wear: Many 4080s were used in mixed gaming/mining rigs. Check for repasted cards and verified hours.
  • Driver support: The 4080 Super is an Ada Lovelace card fully supported by current NVIDIA drivers. No compatibility concerns.
  • FP4 quantization: The 4080 Super does not support FP4 (that’s Blackwell’s feature). Emerging FP4-optimized models won’t benefit on this card. For now (June 2026), Q4_K_M remains the dominant quantization anyway, so this isn’t disqualifying — but it’s a forward-looking consideration.

For a 3-year TCO comparison with the RTX 3090 24GB and RTX 5060 Ti, see our RTX 5060 Ti 16GB vs used RTX 3090 analysis.

Against the field: three-way comparison

4080 Super vs RTX 5060 Ti 16GB

The 5060 Ti 16GB at $429 street is the natural alternative. You get the same 16GB VRAM, but 39% less bandwidth and 56% less throughput on 14B models. The power saving is real — 180W vs 320W means a quieter, cooler rig and $145 less electricity over three years.

If you’re a home lab builder who runs 14B models occasionally, the 5060 Ti is the better buy. If 14B is your workhorse and you’re coding all day, the 4080 Super’s throughput advantage compounds into real time savings.

The 5060 Ti also has an edge on future-proofing: it supports FP4 quantization via Blackwell’s hardware path. When FP4-native model weights become standard (likely 2027+), the 5060 Ti gains performance that the 4080 Super can’t match.

4080 Super vs RTX 5070 12GB

The RTX 5070 at ~$549 new runs Qwen3 8B at 53.4 tok/s and Qwen3.5 9B at 82.9 tok/s with a 32K context window. Faster per token than the 4080 Super on 8B-9B models, cheaper, and lower power draw. The problem: 12GB VRAM is a hard ceiling. Qwen3 14B at Q4_K_M is 9.4 GB — fits with ~2.6 GB spare — but you lose the room for long-context KV cache. At 32K context on Qwen3 14B, the 5070 12GB starts paging.

Pick the 4080 Super over the 5070 if you regularly use models in the 14B–22B Q4 range or run 32K+ context windows.

4080 Super vs RTX 5070 Ti 16GB

This is the real competition. The RTX 5070 Ti has 896 GB/s bandwidth (22% over the 4080 Super’s 736 GB/s), uses GDDR7, supports FP4, draws 300W (vs 320W), and comes with a new warranty. MSRP is $749 — but actual street prices in June 2026 are hovering around $979 due to supply constraints, according to bestvaluegpu.com and retail tracking.

At $979 new vs $860 used for the 4080 Super, the 5070 Ti is $119 more. For that premium you get:

  • ~22% faster token generation
  • FP4 support for next-gen quantized models
  • Full warranty and new hardware
  • 20W less power draw

The case for the 4080 Super over the 5070 Ti only holds if you can find the Super at $800 or below AND the 5070 Ti remains above $900. That window has been open in the used market for several months. If supply loosens and the 5070 Ti drops to $800-850 new, the 4080 Super loses its only advantage.

Check the 5070 Ti stock before pulling the trigger on a used 4080 Super. If you can get the 5070 Ti within $100 of the 4080 Super’s asking price, take the new card.

Who should buy the RTX 4080 Super

Yes, if:

  • You run Qwen3 14B, Mistral Small 22B, or similar 14B–22B models as your daily driver
  • You’ve checked and the 5070 Ti is $200+ over MSRP in your region
  • You’re buying from a reputable seller with clear usage history
  • You’re comfortable with Ada Lovelace being one generation old
  • 320W in your home lab setup is fine for your PSU and cooling

No, if:

  • Your primary models are 7B–9B (the 5060 Ti is faster per dollar for you)
  • You care about FP4 compatibility for future model formats
  • You’re on a tight budget and need to maximize VRAM/dollar
  • The 5070 Ti is available near MSRP in your area

The 4080 Super is the right card for a specific person: someone who has outgrown the 5060 Ti’s throughput, runs models in the 14B-20B range daily, doesn’t want to pay 5070 Ti prices, and is comfortable buying used. That’s a narrower profile than it first appears, but it’s real.

FAQ

Can the RTX 4080 Super run Llama 3.3 70B? Not at useful quality. The 70B model in Q4_K_M needs ~41 GB of VRAM — almost 3× what the 4080 Super holds. You can run heavily compressed versions (IQ1_S at ~14 GB) but quality drops substantially. For 70B, look at the RTX 3090 24GB or a dual-GPU NVLink setup.

Is the RTX 4080 Super good for fine-tuning? Yes, for LoRA/QLoRA on 7B–13B models with 4-bit base weights. With 16GB, you can train a Q4 7B model with a 4K context batch without offloading. Our QLoRA cost analysis runs the numbers on 4090 vs RunPod — the 4080 Super sits roughly 30% behind the 4090 in fine-tuning throughput.

Does Ollama run well on the 4080 Super? Yes. Ollama fully supports Ada Lovelace cards via CUDA 12.x. Install the standard NVIDIA driver (≥560), install Ollama, and it detects the card automatically. No extra configuration needed.

How does it compare to the Mac Studio M4 Max? The Mac Studio M4 Max 128GB runs Qwen3 14B at approximately 30 tok/s with its 546 GB/s unified memory bandwidth, per community benchmarks. The 4080 Super’s ~62 tok/s on the same model is about 2× faster — but the Mac Studio fits 70B and larger models in unified memory and uses 90% less power at idle. Different tools for different jobs. See the Mac Studio M4 Max vs Mac Mini M4 Pro comparison for Apple Silicon context.

Is the RTX 4080 Super future-proof? For LLM inference, Ada Lovelace will be useful for several more years. The biggest forward-looking risk is FP4 quantization support — Blackwell (50 series) handles FP4 in hardware, which could enable 2× effective throughput on future model formats. GDDR6X also won’t benefit from the same per-GB efficiency gains as GDDR7. Neither concern is a dealbreaker today, but the 5070 Ti is meaningfully more forward-looking.

Sources

Last updated June 7, 2026. Prices and used-market availability shift weekly; verify current eBay completed listings before purchasing.

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