Is 8GB VRAM enough for local LLMs in 2026?

8GB works fine for 7B models (Mistral, Llama 2, Qwen 2.5 7B) at 12-18 tokens/sec. For 14B models like Qwen 2.5 14B, you'll hit memory limits even with Q4 quantization. Start with 8GB only if you're committed to 7B models; otherwise, 16GB at $429 saves you the frustration of realizing mid-project that you need to upgrade.

Does quantization let me run bigger models on 8GB?

Quantization helps, but it's not magic. Q4 quantization reduces Qwen 2.5 14B from 28 GB to ~9 GB, but 8GB total system VRAM (with OS overhead) means you're swapping to disk at that point—your tokens/sec plummets from 12+ to 2-3. You'll regret it within a week.

Should I buy RTX 5060 Ti or jump to RTX 5070?

If you're running 7B models consistently, 5060 Ti 16GB ($429) is the right pick. If you're experimenting with 70B models or want futureproofing, the 5070 12GB ($549) gives you more VRAM and 25% better performance for only $120 more. The 8GB 5060 Ti is the trap—skip it entirely.

What's the best 'first GPU' for local LLM work right now?

RTX 5060 Ti 16GB ($429) if you have a tight budget. RTX 5070 12GB ($549) if you can spend $120 more—you get more memory and better performance for the same price-per-GB. Avoid the 8GB variant; the $50 saved today costs you a GPU upgrade tomorrow.

The 8GB VRAM Trap: Why Your RTX 5060 Ti Might Cost You Twice

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 8GB Mirage: Why It Feels Reasonable Until It Isn't

TL;DR: The RTX 5060 Ti's 8GB variant ($379) is a trap dressed up as a budget pick. You can run Mistral 7B or Llama 2 7B fine—18+ tokens/sec, no sweat. But the moment you try Qwen 2.5 14B or anything in the 14-32B range, 8GB hits a wall so hard you'll regret not spending $50 more on 16GB. Save yourself the pain: get the 16GB version at $429 or jump to RTX 5070 for $549.

8GB was the "sweet spot" in 2024-2025 when most local LLMs maxed out at 13B parameters. But 2026 shifted the game. Model creators consolidated around 7B, 14B, and 32B tiers—skipping the middle ground that 8GB used to comfortably handle.

The real problem? Marketing parity. An 8GB card sounds reasonable for local AI because older guidance (from 2024) said so. Retailer descriptions say "run Llama locally" without mentioning which Llama. Budget builders see $379 and think they're getting the deal of the century, then discover during their first real workload that they picked wrong.

What Changed Between 2025 and April 2026

Model consolidation killed the 7B→13B→33B ladder. Now it's 7B→14B→32B, with nothing in between:

Meta Llama 3.1: Went 8B, 70B, 405B. No 13B. (They learned from Llama 2's oversupply of sizes.)
Qwen 2.5: Shipped 7B, 14B, 32B, 72B. Again, no 13B sweet spot.
Mistral: Similar consolidation—7B fine-tunes, then jumps to Large and Nemo (22B+).

This means the VRAM gap is real and sudden. You can't "just quantize a bit more" to bridge it.

What Actually Fits in 8GB (The Honest List)

Three models reliably fit 8GB with acceptable inference speed (10+ tokens/sec). Everything else either doesn't fit or limps along at 3-5 tokens/sec.

The 8GB Sweet Spot (Q4 quantization):

Usability

Excellent

Good

Borderline That's it. That's the list.

Anything beyond that—Qwen 2.5 14B, Llama 70B, Deepseek 33B—doesn't fit in 8GB without severe performance penalties.

Why 8 tokens/sec Feels Like a Ripoff

At 8 tokens/sec, a 64-token response takes 8 seconds. For comparison:

12 tokens/sec (what you get on 16GB) = 5.3 seconds
16 tokens/sec (what you get on 5060 Ti 16GB with 7B) = 4 seconds

That gap—3-4 seconds per response—is perceptible. You notice the slowdown. Interactive work becomes frustrating. By week two, you're thinking "I should've just spent the extra $50."

The Breaking Point: Models That Don't Fit

Qwen 2.5 14B: The First Real Wall

Qwen 2.5 14B is 28 GB in native BF16. Even at Q4 quantization (the most aggressive sensible compression):

Q4 file size: ~9 GB
Total VRAM with KV cache + overhead: 10-11 GB
Result on 8GB GPU: Memory errors or disk swapping (1-2 tokens/sec—unusable)
Result on 16GB GPU: 11-13 tokens/sec (perfectly usable)

This is the 8GB killer. It's the model people ask about ("Can I run Qwen 2.5 locally?"), and the honest answer for 8GB is "no, not well."

Qwen 2.5 32B: The Budget Builder's Temptation

32B is tempting because it's free to download and (theoretically) smarter than 14B. In practice:

Q4 file size: ~18 GB
Total VRAM needed: 22-24 GB
Practical minimum GPU: RTX 4090, RTX 5080, or multi-GPU setup

This isn't an 8GB conversation. It's not even a 16GB conversation.

Llama 3.1 70B: The "Why Am I Even Looking At This" Tier

If you're tempted by 70B models, stop. You're not thinking clearly.

Native size: 140 GB
Q4 smallest viable: 32-36 GB
Practical GPU: RTX 5080 16GB minimum (paired with very aggressive quantization), or dual 24GB cards

You're in RTX 5070 Ti and above territory. The article about 8GB vs 16GB doesn't apply to you.

The True Cost: 8GB vs 16GB Showdown

Specs and Pricing (April 2026)

Performance Tier

Entry

Entry+

Mid-range

Mid+ Notice the price-per-GB math: the 16GB variant of the 5060 Ti is actually cheaper per gigabyte ($27/GB vs $47/GB). You're paying a premium for constraints when you buy 8GB.

Performance at 16GB

The jump from 8GB to 16GB opens up an entire tier of models:

Llama 3.1 8B (Q5): 14-16 tokens/sec (usable with slightly lower quantization)
Qwen 2.5 14B (Q4): 11-13 tokens/sec (now actually works)
Llama 70B (Q3): 3-4 tokens/sec on RTX 5060 Ti 16GB (technically runs, but barely)

The models that were unusable or non-existent on 8GB become functional on 16GB.

The RTX 5070 has 12GB and costs $549—only $120 more than 16GB 5060 Ti. It's ~25% faster on LLM inference. The math:

Scenario A: RTX 5060 Ti 16GB ($429) running Qwen 2.5 14B: 12 tokens/sec
Scenario B: RTX 5070 12GB ($549) running the same model: 15 tokens/sec
Cost difference: $120 for 25% more speed

For daily coding work or content generation, that speed bump is worth it. For occasional experimentation, 5060 Ti 16GB is fine.

Warning

The RTX 5060 Ti 8GB is the trap. It's not a budget win—it's a false economy. You're saving $50 today to spend $429 on an upgrade in 6 months.

The Decision Tree: What to Actually Buy

Path 1: "I Just Want Local LLMs and Don't Know What to Run"

→ RTX 5060 Ti 16GB ($429)

Safe pick. Runs everything up to 14B models decently. You won't hit a wall in the first year.

What you get: Mistral 7B, Llama 2 7B, Qwen 2.5 7B (all 16-18 tokens/sec). If you level up to Qwen 2.5 14B, you get 11-13 tokens/sec—acceptable.

Path 2: "I Want Futureproofing and Can Spend a Bit More"

→ RTX 5070 12GB ($549)

Spend the extra $120. You get:

25% better performance on every model
50% more VRAM than the 8GB trap (though less than 5060 Ti 16GB)
Better value long-term if you experiment with 32B models later

Path 3: "I Have a 10-Year-Old Gaming Rig and Want to Test Local AI"

→ RTX 5060 Ti 8GB ($379) — only if you're genuinely only testing Mistral 7B for a week

Test the waters. If you like it and want to keep going, upgrade immediately to 16GB. Don't plan around 8GB.

Path 4: "I'm Serious About 70B+ Models"

→ RTX 5070 Ti 16GB ($749) or RTX 5080 16GB

You're not buying a GPU for Qwen 2.5 14B anymore. You're buying for scale. Go bigger or go home.

The Quantization Myth: "I'll Just Compress More"

Quantization is lossy compression. Q4 is the line where models stay sane. Go Q3 and you start losing reasoning quality. Q2 is basically gibberish with occasional lucidity.

On 8GB, people get desperate:

"I'll just run Qwen 2.5 14B at Q3 on my 8GB card."

Here's what actually happens:

Q3 file size for Qwen 2.5 14B: ~7 GB (barely fits)
Actual tokens/sec: 2-3 (you're staring at the screen for 30+ seconds waiting for an 8-sentence response)
Output quality: Degraded. Hallucinations increase. Logic breaks.

It's technically possible but practically unusable. Don't do this to yourself.

Real-World Comparison: 8GB vs 16GB in Practice

Day 1: Both setups running Mistral 7B

8GB: 17 tokens/sec ✓
16GB: 18 tokens/sec ✓ (Basically identical)

Week 2: Trying Qwen 2.5 14B

8GB: Memory error or 1-2 tokens/sec (unusable)
16GB: 12 tokens/sec (actually works)

Day 21: Buyer's remorse

8GB owner: "I need to upgrade. Again."
16GB owner: "I'm good. Moving on."

The 8GB buyer saves $50 upfront and pays $429 in replacement hardware within a month. That's not a budget pick—that's a trap.

FAQ: Busting the 8GB Myths

"Can't I just underclock to save VRAM?"

No. Underclocking reduces speed, not memory usage. You still need the same amount of VRAM to load the model; you just load it slower.

"If I upgrade my system RAM to 64 GB, can I use that as backup VRAM?"

Technically, yes (NVMe fallback). Practically, you'll see a 100x slowdown. You'll get 0.1 tokens/sec instead of 10. Don't.

"What if I buy 8GB and upgrade later?"

You're buying a GPU twice. Older GPUs have poor resale value after 12 months. Just spend the $50 now.

"Is a used RTX 4070 Ti cheaper than new 5060 Ti 16GB?"

Maybe. Older GPUs run hotter, have fewer features (no AV1 encoding), and you lose warranty. The new 5060 Ti 16GB at $429 is safer. Verify used pricing on the day you buy—GPU markets shift weekly.

"Aren't there other brands cheaper than NVIDIA?"

AMD's RDPU line is cheaper but less software support for Ollama/llama.cpp. Intel Arc is arriving late to the party. For local LLM work specifically, NVIDIA dominance means better driver support and faster inference. Don't save $30 on a GPU to lose $2 of performance per token.

Your Next Move

Bought 8GB already? Check your return window. Spend the extra $50 if you can.
Still deciding? Buy 16GB. You'll thank yourself week 2.
Want the best value? RTX 5070 12GB at $549 is the real sweet spot for 2026—more VRAM, better speed, future-proof.
Serious about AI infrastructure? Skip this tier entirely. Go RTX 5080 or multi-GPU.

The 8GB trap is real. But it's avoidable if you know what's coming. Don't be the person posting "why is my RTX 5060 Ti 8GB so slow?" to r/LocalLLaMA. Buy the right GPU the first time.