This article was originally published on runaihome.com
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 16GB | RTX 4080 Super (used) | RTX 5070 Ti 16GB | |
|---|---|---|---|
| Best for | Budget 8B–14B inference | 14B model speed, used value | Maximum 16GB throughput |
| Price | ~$429 new | ~$860 used | ~$979 actual ($749 MSRP) |
| Bandwidth | 448 GB/s GDDR7 | 736 GB/s GDDR6X | 896 GB/s GDDR7 |
| Qwen3 14B @ 16K ctx | 32.9 tok/s | ~61 tok/s | ~75 tok/s (est.) |
| TDP | 180W | 320W | 300W |
| The catch | Bandwidth-limited on 14B+ | Used only, 2× the power draw | Supply-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:
| Model | Quantization | VRAM Used | Speed |
|---|---|---|---|
| Llama 3.1 8B | Q8_0 | 8.5 GB | ~95 tok/s |
| Qwen3 14B | Q4_K_M | 9.4 GB | ~62 tok/s |
| Qwen3 14B | Q6_K | 11.8 GB | ~55 tok/s |
| Mistral Small 3 22B | Q4_K_M | 13.3 GB | ~41 tok/s |
| Llama 3.3 70B | Q2_K | 28 GB | ❌ CPU offload |
| Llama 3.3 70B | IQ1_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:
| Card | Daily | Monthly | Annual | 3-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). Emergin
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