RTX 5060 8GB vs RTX 4070 12GB for Qwen3 14B: Does 8GB Fit? (2026)

As an Amazon Associate, SpecPicks earns from qualifying purchases. See our review methodology.

Why Qwen3 14B is where 8 GB cards start to struggle

On SpecPicks, the graphics-card comparison readers view most often is the Gigabyte GeForce RTX 5060 WINDFORCE OC 8G against the Gigabyte GeForce RTX 4070 WINDFORCE OC 12G. Most of those shoppers are gamers. A growing share also want to run a local model on the same card: a coding assistant, a private chat, a notes summarizer. For that second job, 14B is where the two cards stop being close.

Up to 8B, both cards are fine. Qwen3 8B at Q4_K_M is 5.03 GB, which fits in 8 GB with room to spare. The RTX 5060 runs it at 61.7 tok/s in DatabaseMart's Ollama tests. That's a very good number for a 145 W card. DatabaseMart stopped at 8B, and there's a reason for that.

Qwen3 14B is the step up that most people make next. According to the Qwen3 release post, it's a dense 14.8B-parameter model with thinking and non-thinking modes and a 32K native context. It's noticeably better than 8B at multi-step reasoning, code and following long instructions. It's also the first Qwen3 size whose standard Q4 build is larger than 8 GB. Once a model doesn't fit in VRAM, the GPU's own bandwidth stops setting your speed. The PCIe link and system RAM do. In practice that makes the 5060's GDDR7 advantage worthless for this model.

So the question isn't which card is faster. It's whether you're willing to run 14B in a compromised form on an 8 GB card, or pay for 12 GB so it runs as intended.

Key Takeaways

• Qwen3 14B Q4_K_M is 9.00 GB before any KV cache. It can't fit in 8 GB (unsloth GGUF).
• The RTX 4070 runs it at 42.5 tok/s at 4K context and 32.7 tok/s at 16K (Hardware Corner).
• An RTX 3060 12GB runs it at 31.2 tok/s at 4K (Hardware Corner), for about half the RTX 4070's launch price.
• On the RTX 5060, your realistic options are IQ3/Q3 quants (6.0–7.3 GB), with some quality loss, or partial CPU offload. We estimate offload at roughly 12–18 tok/s.
• For 8B models the RTX 5060 is excellent: 61.7 tok/s on Qwen3 8B at 145 W (DatabaseMart).

Step 0: Which Qwen3 size do you actually need?

Before you spend on VRAM, check whether 14B is the model you need:

• Qwen3 8B is enough for chat, email drafts, summaries, and autocomplete-style coding help. It fits any 8 GB card. If this describes you, buy the RTX 5060 and stop reading.
• Qwen3 14B is the step up for multi-file code reasoning, longer instructions, better tool calling, and thinking mode on harder problems. It needs 12 GB to run as intended.
• Qwen3 30B-A3B is a mixture-of-experts model that activates only about 3B parameters per token. At Q4 it's about 18.6 GB, too big for either card on its own. It runs well split across a 12 GB GPU and system RAM, and even CPU-only (see our Ryzen 5 2600 vs Ryzen 7 5800X CPU-only Qwen3 30B-A3B test).

If you aren't sure, try Qwen3 8B first. If it keeps falling short on your real tasks, you'll know you need 14B, and that you need 12 GB.

Spec delta: RTX 5060 8GB vs RTX 4070 12GB

Specs are from NVIDIA's RTX 5060 family and RTX 4070 family pages and TechPowerUp's RTX 5060 and RTX 4070 database entries.

One row deserves attention: PCIe 5.0 x8. On a PCIe 4.0 motherboard, which describes most AM4 and Intel 12th/13th-gen builds, the RTX 5060 runs at 4.0 x8, about 16 GB/s. That link is exactly what offloaded layers travel over, so it limits the 5060 in the one scenario Qwen3 14B forces on it.

How much VRAM does Qwen3 14B need at each quantization?

File sizes are the unsloth GGUF builds. The VRAM column adds an fp16 KV cache at 4K context and about 0.7 GB of runtime buffers. Qwen3 14B has 40 layers and 8 KV heads of dimension 128, so the KV cache costs 160 KB per token, or 0.67 GB at 4K.

The practical ceiling on the RTX 5060 is IQ3_XXS with a short context. On the RTX 4070 it's Q4_K_M at up to about 16K, or Q5_K_M at short context.

What happens when the model spills out of 8 GB?

llama.cpp lets you choose how many layers go on the GPU with -ngl. Ollama does the same thing automatically when a model doesn't fit. Once you leave room for the KV cache and CUDA buffers, only about 30 of Qwen3 14B's 40 layers fit on the RTX 5060. The remaining ten, about 2.25 GB of Q4_K_M weights, run on the CPU from system RAM.

Now every generated token has to read those 2.25 GB from DDR4 or DDR5 at maybe 40–70 GB/s of real-world bandwidth, instead of from GDDR7 at 448 GB/s. It's also processed by CPU cores, not CUDA cores. Here's our estimate of per-token time:

• GPU portion (~6.75 GB at ~65–75% of 448 GB/s): about 20–23 ms
• CPU portion (~2.25 GB at 40–70 GB/s effective): about 32–56 ms
• Synchronization and PCIe hand-off: a few ms

That's about 55–85 ms per token, or roughly 12–18 tok/s. It's usable, but it's a third to less than half of the RTX 4070's 42.5 tok/s. Prefill suffers more, because every prompt token also crosses the x8 PCIe link. These are SpecPicks estimates from bandwidth arithmetic, not measurements. Actual results vary by workload, CPU and RAM speed.

Tokens per second on each card

The 8B rows are the control. On a model that fits, the 5060 lands between the 3060 and the 4070, as its 448 GB/s bandwidth predicts. The 14B rows show what happens when it doesn't fit.

How does context length change the verdict?

At 160 KB per token, the Qwen3 14B KV cache costs 0.67 GB at 4K, 2.68 GB at 16K and 5.37 GB at 32K (fp16).

Quantizing the KV cache to q8_0 roughly halves the cache column. That's the easiest way to give the RTX 4070 comfortable 16K headroom. On the RTX 5060 it doesn't help much, because the weights alone already overflow the card.

Is the older RTX 3060 12GB the smarter middle ground?

For this specific model, often yes. The MSI GeForce RTX 3060 12GB and the more compact ZOTAC RTX 3060 Twin Edge OC 12GB have the same 12 GB ceiling as the RTX 4070. They fit Qwen3 14B Q4_K_M entirely in VRAM and generate 31.2 tok/s at 4K (Hardware Corner). That's about 25% slower than the 4070, with 360 GB/s of bandwidth against 504 GB/s, but it's roughly twice what the RTX 5060 manages with offload.

The 3060's launch MSRP was $329, and used cards sell well below Amazon's current new-card listings. The trade-offs: it's an older Ampere card, it's weaker for gaming than the RTX 5060 (no DLSS 4 frame generation), and it draws 170 W against the 5060's 145 W. If local 14B inference matters more to you than gaming frame rates, it's the value pick.

Performance per dollar and per watt math

Using launch MSRPs (street prices swing too much to be a stable basis) and total graphics power:

The RTX 3060 12GB wins on value. The RTX 4070 wins on efficiency and raw speed. On this model, the RTX 5060 is last on both.

Can these cards game and run a local LLM on the same PC?

Yes, just not at the same time if you want good frame rates. Both cards are strong 1080p gaming GPUs, and the RTX 5060 has DLSS 4 multi-frame generation, which the 4070 lacks. Be aware that Ollama keeps a model loaded in VRAM for five minutes after the last request by default. If you launch a game right after a chat session, the model is still holding several gigabytes of VRAM. Run ollama stop <model> first, or set OLLAMA_KEEP_ALIVE=0. On an 8 GB card this matters even more, because modern games already push past 8 GB at high texture settings.

Verdict matrix

Get the RTX 5060 8GB if…

• Your local LLM work runs on 8B-class models, where it hits 61.7 tok/s
• Gaming is the main job and the LLM is a side project
• You want the lowest power draw, or your budget is capped near $300

Get the RTX 4070 12GB if…

• You want Qwen3 14B at Q4 with no compromises, fully on the GPU at 42.5 tok/s
• You use thinking mode or coding agents, where speed adds up over thousands of tokens
• You also want the fastest prompt processing: 2,099.9 tok/s at 4K on 14B

Get an RTX 3060 12GB if…

• 14B quality is the goal and budget comes first
• You're building a dedicated always-on inference box, not a gaming rig

Recommended pick for Qwen3 14B

Buy the RTX 4070 12GB if your budget stretches to it. It's the only card of the three that runs Qwen3 14B at the standard Q4_K_M quality, fully in VRAM, at over 40 tok/s, and it still handles 16K context. If the budget doesn't stretch that far, a used or discounted RTX 3060 12GB gives you the same 14B capability at about 73% of the speed. The RTX 5060 8GB is the wrong card for this model. It's a great card for 8B, so if 8B covers your work, buy it without hesitation.

Bottom line

8 GB isn't enough for Qwen3 14B as intended. The 9.00 GB Q4 file forces either a lower-quality quant or a CPU offload that cuts speed by more than half. 12 GB is the practical floor for 14B, and the RTX 4070 is the fastest way to get there among these cards.

Related guides

• RTX 5060 vs RTX 3060 12GB for Local LLMs in 2026
• RTX 3060 12GB vs RTX 4070 for Qwen2.5 14B
• RTX 3060 12GB vs Ryzen 7 5800X for Qwen3 14B
• Best GPU for Qwen3 8B in 2026

Live price comparison

See current prices for both cards side by side: RTX 5060 8GB vs RTX 4070 12GB live comparison.

Citations and sources

1. Qwen Team, Qwen3: Think Deeper, Act Faster — model sizes, context length, thinking modes. Accessed September 24, 2026.
2. unsloth/Qwen3-14B-GGUF — quantization file sizes. Accessed September 24, 2026.
3. NVIDIA, GeForce RTX 5060 Family — RTX 5060 specifications. Accessed September 24, 2026.
4. NVIDIA, GeForce RTX 4070 Family — RTX 4070 specifications. Accessed September 24, 2026.
5. TechPowerUp GPU Database, RTX 5060 and RTX 4070 — bus width, bandwidth, PCIe lanes. Accessed September 24, 2026.
6. Hardware Corner, RTX 4070 and RTX 3060 12GB local LLM benchmarks — Qwen3 8B and 14B generation and prefill. Accessed September 24, 2026.
7. DatabaseMart, RTX 5060 Ollama Benchmarks — Qwen3 8B eval rate on the RTX 5060. Accessed September 24, 2026.

This article is an editorial synthesis of the published benchmarks and manufacturer specifications cited above; offload speeds and VRAM totals are SpecPicks calculations and are labeled as estimates.