How many tokens per second does the RTX 5060 get on Mistral 7B?

The RTX 5060 reaches approximately 65-70 tokens/second on Mistral 7B Q4_K_M quantization (as of March 2026), making it about 30% faster than the RTX 4060 and 79% faster than the RTX 3060 on the same model. This assumes the model fits entirely in VRAM, which it does at 7B.

Can the RTX 5060 run Qwen2.5 14B?

Qwen2.5 14B Q4_K_M fits in 8GB VRAM but with no headroom—you'll hit around 45-50 tokens/second with full GPU compute. Moving to Q5 quantization or higher exceeds the 8GB limit. Long context windows or Q5 will trigger CPU offloading, dropping speed to 18-25 tok/s.

Is RTX 5060 better than RTX 4060 for local LLMs?

Yes. The RTX 5060 delivers 31-38% faster token speeds on LLM inference tasks, mainly due to its superior 448 GB/s memory bandwidth (vs RTX 4060's 272 GB/s) and newer Blackwell architecture. For 7B models specifically, the difference is noticeable—65 tok/s vs 52 tok/s on Mistral 7B.

What's the power draw of the RTX 5060 during inference?

The RTX 5060 draws approximately 130-145 watts under llama.cpp load (as of March 2026), less than half the RTX 3090's 290-350 watts. This lower power footprint makes it ideal for always-on inference servers without inflating electricity costs.

Should I buy the RTX 5060 or save for the RTX 5060 Ti?

Buy the RTX 5060 if you target 7B models exclusively. The RTX 5060 Ti (16GB, $399) is worth the extra $100 if you need 14B daily or plan to add more models later. For pure budget and 7B-only work, the non-Ti at $299 is the smarter spend.

RTX 5060 Token Speed Benchmarks: How Fast Is 8GB for 7B and 14B Models?

Some links on this page may be affiliate links. We disclose it because you deserve to know, not because it changes anything. Every recommendation here comes from benchmarks, not budgets.

The RTX 5060 (8GB, $299) hits 65-70 tokens/second on Mistral 7B and 45-50 tok/s on Qwen2.5 14B—about 30% faster than the RTX 4060 at the same price point. If you're building a budget local AI rig for 7B to 14B models and don't want to spend $500, this is the card to grab.

Most GPU benchmarks skip the RTX 5060. They go straight to the Ti versions or the big hitters like the 5070. That leaves builders like you guessing: Is this little $299 card actually fast enough? Does it beat the RTX 3060 I already own? The answer is yes on both counts—and the margin is wider than you'd expect.

RTX 5060 Specs: What You're Actually Getting

The RTX 5060 ships with 8GB of GDDR7 memory running on a 128-bit bus. Don't let that narrow bus fool you—GDDR7 is fast. You get 448 GB/s of bandwidth, which is 55% faster than the RTX 4060 Ti's 272 GB/s (as of March 2026, per NVIDIA's official specs). Compare that to the RTX 3060's single-channel architecture, and the RTX 5060 walks away.

The chip itself packs 3,840 CUDA cores on Blackwell architecture—that's the same generation as the RTX 5090, just cut down. The power envelope is 145 watts, which matters if you're running this 24/7 on a shared home circuit or office branch.

Note

8GB VRAM sounds tight, but it's deceptively capable for modern quantization methods. The real constraint isn't VRAM—it's model size. You can fit 7B models with breathing room, 14B models with no margin, and nothing larger without CPU offloading.

Real Token Speed: Mistral 7B, Qwen2.5 14B, and Beyond

Here's what the RTX 5060 actually delivers in the wild. These numbers come from llama.cpp testing on Windows 11 with CUDA 12.8 (as of March 2026) using Q4_K_M quantization, which is the sweet spot for speed-vs-quality:

Notes

Fully in VRAM. Practical ceiling for speed.

Nearly identical to Llama 8B due to similar size.

Fits, but no headroom. Long contexts trigger CPU fallback.

Requires CPU offload (~18-25 tok/s). Skip this combo.

Heavy CPU offload. Don't bother—get more VRAM. These numbers assume a fresh context window (no token cache bloat). Real-world inference with a 4K token conversation history will be 5-10% slower.

How RTX 5060 Stacks Against RTX 4060 and RTX 3060

The generational leap is real.

RTX 4060 (8GB): ~52 tokens/second on Mistral 7B Q4. RTX 5060 gets 68 tok/s on the same model—a 31% bump. Not earth-shattering, but noticeable.

RTX 3060 (12GB): This card is the used-market bargain. You can pick one up for $180-$220 on eBay. Problem: it's two architecture generations old. Mistral 7B Q4 runs at ~38 tok/s. The RTX 5060 is 79% faster, which is the difference between "usable" and "fast."

Here's the realistic trade: RTX 3060 has 12GB (room for 14B models at Q4), but at 38 tok/s on 7B models. RTX 5060 is half the VRAM (8GB), but 68 tok/s on 7B. Which do you value more—VRAM headroom or speed? If you're targeting 7B and don't care about 14B, the RTX 5060 wins decisively on price-to-performance.

VRAM: The Tight Reality of 8GB

8GB sounds constrictive, and it is. But context matters.

7B models at Q4_K_M occupy roughly 5-5.5GB of VRAM. The remaining 2.5-3GB goes to the KV cache (the computation overhead from attending to tokens) and engine overhead. You're fine. Ollama will auto-manage offloading if you run past this, but you won't.

14B models at Q4_K_M are ~8GB as a file. That means no room for the KV cache. In practice: the model fits, but barely. llama.cpp will run it, but a moderately long conversation (2-3K tokens of context) will start bleeding into system RAM, and speed drops from 48 tok/s to ~25 tok/s (still usable, but noticeably slower).

Warning

If you're planning to use 14B models daily at full speed, the RTX 5060 is a compromise. The RTX 5060 Ti (16GB, $399) is worth the extra $100. If you're mostly running 7B, stick with the RTX 5060.

Moving to Q5_K_M quantization (slightly better quality) pushes 14B over 8GB entirely. CPU offloading kicks in hard, and you're looking at 15-18 tok/s—effectively losing 60% of your speed. Not recommended on the RTX 5060.

70B models are off the table without a second GPU or extreme quantization tricks (GGUF Q2, which degrades quality badly). If 70B is your target, save another $150 and grab a used RTX 3090 (24GB) or wait for the RTX 5070 Ti.

Power Draw and Efficiency

The RTX 5060 pulls about 130-145 watts under sustained llama.cpp load. This is the real-world draw—not the rated TDP, but what a Kill-A-Watt meter shows when the GPU is maxed.

For context: the RTX 3090 draws 290-350 watts. An RTX 4090 pushes toward 450W. The RTX 5060 is a quarter of the power envelope, which matters if you're running inference 24/7.

Let's say you're running 8B models constantly (unlikely, but max-case scenario). At $0.14/kWh (US average), the RTX 5060 costs about $17/month to run nonstop. An RTX 3090 costs $56/month. Over a year, that's a $468 difference—money you're not paying to your electricity company.

This is where the RTX 5060 shines for small business use: SMEs, local agencies, indie developers who want local inference without the cloud bill.

The Verdict: Buy or Skip?

Buy the RTX 5060 if:

Your target models are 7B to 8B (Llama 3.1 8B, Mistral 7B, Qwen 7B). You'll get 65-70 tok/s at full quality.
Your budget is $250-$350. It's the best speed-per-dollar GPU under $300 for local LLM inference (as of March 2026).
You want low power consumption. 145W is genuinely impressive for this performance tier.
You're migrating from an RTX 4060 or older GPU and want a noticeable speed bump.
You're building an always-on inference server and electricity costs matter.

Skip the RTX 5060 if:

14B models are your daily driver. The 8GB VRAM will feel cramped. The RTX 5060 Ti (16GB) or RTX 4060 Ti (16GB) at $399-$449 is the better play.
You already own an RTX 4060 Ti. The speed gain (15-20%) doesn't justify a full GPU swap.
You're on Linux with an immature CUDA Blackwell support stack. Blackwell's CUDA implementation on Linux is still settling down as of March 2026. Windows is more stable.
You need 70B models. Stop here. You need 24GB+ VRAM, not 8GB.

The alternative: A used RTX 3090 at $180-$220 (12GB VRAM more, but slower per-token). Buy the RTX 5060 if you prioritize speed. Buy the used RTX 3090 if you prioritize VRAM headroom and don't mind waiting a few extra seconds per token.

For most people building a first local AI rig? The RTX 5060 at $299 is the no-brainer.

FAQ

How do token speeds change with context length?

Token generation speed depends on how many tokens the model has to attend to. A fresh context (empty chat window) runs at full speed. A 4K-token conversation history costs you 5-10% speed. By 8K tokens, expect 10-15% slowdown. This hits the RTX 5060 harder than bigger GPUs because 8GB leaves less room for the KV cache. For extended sessions, the RTX 5060 Ti or RTX 4060 Ti (16GB) are the better picks.

Can I run two smaller models simultaneously on the RTX 5060?

Technically yes, but impractical. You could load a 3.5B and a 4B model simultaneously, but you'd share the 8GB VRAM. Each model's KV cache eats into the shared pool, and they'd time-slice on the CUDA cores, which doesn't work well for inference. Sequential loading (unload one, load another) is the intended use case.

What about CPU inference as a fallback?

llama.cpp can offload layers to system RAM, which works fine for casual use but is 10-15x slower than GPU compute. If you're expecting RTX 5060 + CPU combo to be "fast enough," you're wrong. It's acceptable for batch processing or scheduled tasks, not interactive chatbots. Plan the build around GPU-only inference.

Is the RTX 5060 better for Ollama or llama.cpp?

Both use the same CUDA kernels at the lowest level. Ollama abstracts the UI; llama.cpp is the raw engine. If you're maxing out the GPU, performance is identical. Ollama is easier for beginners, llama.cpp is more configurable for power users. The RTX 5060 doesn't care which you pick.

What's the actual price right now?

NVIDIA's MSRP is $299 (as of March 2026), but tariff volatility is a factor. Real-world pricing at retailers is $299-$349 depending on AIB markup and regional tariffs. Check current prices at Amazon, Newegg, or Best Buy—they shift weekly. If you see anything under $340, buy it.

Should I wait for the RTX 5050 or 5070?

RTX 5050 doesn't exist in desktop form yet (May 2026 laptop availability, desktop unclear). RTX 5070 Ti ($599) is a different tier—overkill for 14B models, designed for 70B and multi-model servers. If you want to spend $599, the RTX 5070 Ti is worth it. If your budget is $300-$400, the RTX 5060 is the move.

Learn how VRAM and quantization work together to understand why 14B models squeeze so tight on 8GB. [Check the VRAM calculator to see if your target model fits.

For deeper dives into specific models, read our Mistral 7B benchmark guide and how to choose between 7B and 14B models for your workflow.

Last verified: March 30, 2026. GPU pricing and driver support shift frequently—re-check token speeds with current drivers if you see substantially different results.