The 8GB VRAM Breakthrough: How PagedAttention and Dynamic KV-Cache Quantization Unlock 64k Context on Consumer GPUs

Why spilling even 100 MB to system RAM destroys local LLM throughput by 90%, and how virtualized block tables, asymmetric INT4/FP8 KV-cache quantization, and speculative decoding fit a full 64,536-token context window inside an RTX 3060 or 4060 Ti 8GB.

By Benni Alencar — Chief AI Systems Architect at Benni OS. Open-source implementation available at Benni Inference Engine.

1. The 8GB VRAM Reality Check and the PCIe Memory Cliff

If you run local Large Language Models on consumer hardware—an NVIDIA RTX 3060 8GB, an RTX 4060 / 4060 Ti 8GB, or a laptop RTX 4070 8GB—you have almost certainly encountered the PCIe memory cliff.

At 4,096 tokens of context, a 4-bit quantized 8B model hums along at a snappy 48 to 62 tokens per second. Everything feels instantaneous. You increase the context window to 16k or 32k tokens to feed a codebase or a long PDF, and suddenly your terminal freezes. Throughput collapses from 55 tok/s down to 4.2 tok/s—a >90% performance crash—even though your GPU utilization drops to 15% and your CPU barely breaks a sweat.

What happened? You crossed the 8,192 MB VRAM boundary by as little as 100 MB.

To understand why a 1.2% memory overflow causes a 90% speed collapse, we have to look at the hardware asymmetry between on-package GDDR6 memory and the PCIe bus:

During the autoregressive decode phase (generating one token at a time), LLM inference is overwhelmingly memory-bandwidth-bound, not compute-bound. To generate a single token, the GPU must stream all model weights and the entire accumulated Key-Value (KV) cache from VRAM into the Streaming Multiprocessors (SMs), perform a matrix-vector multiplication with an arithmetic intensity near $1\text{ FLOP/byte}$, and write one new KV vector back.

When the working set fits inside 8GB VRAM, the GPU streams at ~300 GB/s. The moment the runtime spills even 100 MB of KV-cache pages or a single transformer layer to host RAM (via Unified Memory or CPU offloading), every single generated token triggers synchronous host-to-device PCIe transfers. Because the GPU must stall and wait for those bytes over a 12–24 GB/s straw—roughly 15x to 25x slower than GDDR6—your end-to-end decode speed plummets by 90% or more.

Traditional inference runtimes treat this as an unavoidable hardware limit: "Buy a 24GB RTX 3090/4090, or cap your context at 8k."

In Benni Inference Engine (BIE), we rejected that premise. By combining PagedAttention block tables, dynamic mixed-precision KV-cache quantization (Sink-FP16 + Body-INT4/FP8), and memory-aware speculative decoding, you can sustain 64k (65,536 tokens) of context on an 8GB GPU without spilling a single byte across the PCIe bus.

2. First-Principles Math: Why the KV-Cache Eats Your 8GB Alive

Before optimizing memory, we must account for every megabyte inside an 8,192 MB budget.

Consider a modern 8B-parameter model (such as Llama-3-8B or Qwen-2.5-7B/8B) quantized to 4-bit weights (AWQ, GPTQ, or Q4_K_M):

• 4-bit Model Weights: $\approx 4.25\text{ GiB}$
• CUDA Context, cuBLAS/Triton Workspaces & Activation Buffers: $\approx 0.55\text{ GiB}$
• OS Display & Desktop Window Manager Overhead (on a single-GPU workstation): $\approx 0.35\text{ GiB}$

That leaves roughly $2.85\text{ GiB}$ to $3.05\text{ GiB}$ of clean, uncontested VRAM for the KV-Cache.

Now let us compute the exact memory footprint of the KV-Cache in standard FP16 precision using the fundamental KV-Cache equation:

$$\text{VRAM}{\text{KV}} = 2 \times n{\text{layers}} \times n_{\text{kv_heads}} \times d_{\text{head}} \times s_{\text{seq}} \times b \times \text{sizeof}(\text{dtype})$$

Where:

• $2$ accounts for both the Key ($K$) and Value ($V$) tensors.
• $n_{\text{layers}}$ is the number of transformer decoder blocks (e.g., $32$).
• $n_{\text{kv_heads}}$ is the number of Key-Value heads under Grouped-Query Attention (GQA) (e.g., $8$, down from $32$ query heads).
• $d_{\text{head}}$ is the dimension per attention head (typically $128$).
• $s_{\text{seq}}$ is the total sequence length (prompt + generated tokens).
• $b$ is the batch size (number of concurrent sequences).
• $\text{sizeof}(\text{dtype})$ is $2\text{ bytes}$ for FP16/BF16.

Let us plug in the numbers for a single sequence ($b = 1$) in FP16:

$$\text{Bytes per Token} = 2 \times 32 \times 8 \times 128 \times 2 = 131,072\text{ bytes} = 128\text{ KiB/token}$$

At 8,192 tokens, $128\text{ KiB} \times 8,192 = 1.0\text{ GiB}$. Manageable.

At 32,768 tokens, $128\text{ KiB} \times 32,768 = 4.0\text{ GiB}$. Already over our 3.0 GiB budget—PCIe cliff triggered!

At 65,536 tokens (64k), $128\text{ KiB} \times 65,536 = 8.0\text{ GiB}$. The KV-cache alone equals the entire physical VRAM of the GPU.

And notice: that is before accounting for contiguous allocation waste.

3. PagedAttention: Eliminating the 70% Contiguous Fragmentation Tax

In naive PyTorch or early C++ inference engines, the KV-cache for a request is pre-allocated as a single contiguous tensor sized to max_seq_len:

# Naive contiguous pre-allocation (wasteful!) k_cache = torch.empty((batch_size, n_layers, n_kv_heads, max_seq_len, head_dim), dtype=torch.float16, device="cuda") 

If you configure max_seq_len = 65536, the allocator immediately reserves 8.0 GiB even if your current prompt is only 1,200 tokens long. Even worse, if you run two concurrent agentic turns or a multi-branch tree search, you suffer from:

1. Internal Fragmentation: Reserved slots for future tokens that may never be generated.
2. External Fragmentation: Free memory scattered in non-contiguous gaps between active requests that cannot satisfy a new contiguous malloc.
3. Zero Memory Sharing: Identical system prompts or shared tool definitions are duplicated across every active sequence.

Inspired by virtual memory paging in operating systems, PagedAttention breaks the KV-cache into fixed-size physical KV blocks (typically $B = 16$ tokens per block).

How Logical-to-Physical Block Tables Work

Instead of one giant contiguous buffer, BIE pre-allocates a flat Physical Block Pool filling the remaining ~3.0 GiB of safe VRAM. Each sequence maintains a lightweight Block Table on the GPU:

• Logical Block $0$ (tokens $0\dots 15$) $\rightarrow$ Physical Block #412
• Logical Block $1$ (tokens $16\dots 31$) $\rightarrow$ Physical Block #089
• Logical Block $2$ (tokens $32\dots 47$) $\rightarrow$ Physical Block #903

The custom CUDA/Triton attention kernel reads the Block Table on the fly, gathering non-contiguous physical blocks directly into SRAM registers during the $Q K^T$ dot product and softmax-weighted $V$ aggregation.

Copy-on-Write (CoW) Prefix Sharing

In agentic workflows, 2,000 to 8,000 tokens of your context are often a static system prompt, API schemas, or few-shot examples. With PagedAttention, multiple requests—or multiple speculative/beam branches—point their initial logical blocks to the exact same physical blocks with a reference count (ref_count > 1). Only when a branch diverges and writes a new token into a partially filled shared block does the engine perform a Copy-on-Write allocation of a fresh physical block.

PagedAttention reduces KV-cache memory waste from 60–80% down to under 4% (at most $B - 1 = 15$ tokens in the tail block). However, even with zero fragmentation, 64k tokens in FP16 still demands 8.0 GiB. To fit 64k inside 3.0 GiB, we must compress the bytes per token.

4. Dynamic KV-Cache Quantization: FP8 and Sink-Preserving INT4

Can we quantize the KV-cache without lobotomizing the model's reasoning or needle-in-a-haystack recall at 64k tokens?

Yes—if we respect the geometry of attention outliers and Attention Sinks.

Why Naive Global INT4 Fails

Transformer Keys ($K$) exhibit persistent channel-wise outliers: specific feature dimensions carry magnitudes $10\times$ to $50\times$ larger than their neighbors across all tokens. Meanwhile, the very first $4$ to $8$ tokens in a sequence act as Attention Sinks—they absorb disproportionate softmax probability mass to stabilize attention entropy when no semantic match exists in the middle of the context.

If you apply a naive, tensor-wide INT4 quantization scale across all heads and all tokens:

1. Channel outliers stretch the quantization range, crushing normal values into a single bin.
2. Quantization noise on the Attention Sink tokens destabilizes the softmax denominator across all 64,000 downstream tokens, causing perplexity to spike.

The Hybrid Solution: Sink-FP16 + Group-Wise Asymmetric INT4 / FP8

In Benni Inference Engine, each physical KV block carries precision metadata, enabling Dynamic Mixed-Precision KV Quantization:

1. FP8 (E4M3) Mode (2x Compression):
   
   Each 16-bit FP16 value is cast to 8-bit IEEE/OCP float8_e4m3fn with a per-head dynamic scaling factor. This halves KV-cache memory from $128\text{ KiB/token}$ to $64\text{ KiB/token}$ with virtually zero measurable perplexity loss ($\Delta\text{PPL} < 0.03$). At 64k tokens, FP8 requires $4.0\text{ GiB}$—ideal for 12GB GPUs, or up to ~45k tokens on an 8GB GPU.

2. Sink-Preserving Group-Wise INT4 Mode (~3.8x Compression):
   
   To unlock the full 64k (65,536 tokens) on an 8GB GPU:

   • Sink Window (first 8 tokens) & Local Sliding Window (most recent 128 tokens): Retained in FP16 inside dedicated high-precision blocks so attention sinks and immediate syntactic context suffer zero quantization error.
   • Middle Context Body: Quantized per-head, per-group (group size $g = 64$ elements along $d_{\text{head}}$) using asymmetric INT4 ($q \in [0, 15]$) with an FP16 scale $s$ and zero-point $z$: $$x_{\text{dequant}} = (q - z) \cdot s$$

Let us calculate the exact memory footprint per token for Group-Wise INT4 ($g = 64$, $d_{\text{head}} = 128$):

• Each group of $64$ values takes $64 \times 4\text{ bits} = 32\text{ bytes}$ for packed INT4 nibbles, plus $4\text{ bytes}$ for FP16 $(s, z)$ metadata = $36\text{ bytes}$ per $64$ elements ($4.5\text{ bits/element}$ effective).
• Across $2 \times 32 \times 8 \times 128 = 65,536$ elements per token: $$\frac{65,536}{64} \times 36\text{ bytes} = 36,864\text{ bytes} = 36\text{ KiB/token}$$
• At 65,536 tokens (64k): $$65,536 \times 36\text{ KiB} \approx 2.25\text{ GiB}$$

2.25 GiB! Combined with 4.25 GiB for 4-bit model weights and 0.55 GiB for CUDA workspaces, total VRAM sits at ~7.05 GiB—leaving a >900 MB safety margin under the 8,192 MB ceiling on an RTX 3060 / 4060 Ti 8GB. Zero PCIe spills. Zero throughput collapse.

5. System Architecture: Benni Inference Engine (BIE)

Here is how the complete pipeline operates inside BIE to coordinate PagedAttention, dynamic KV quantization, and speculative decoding without ever crossing the PCIe cliff:

flowchart TD Req["Incoming Prompt (Up to 64k Tokens)"] --> PrefixCache{"Prefix Cache Lookup<br/>(CoW Block Table)"} PrefixCache -->|Cache Hit| ReuseBlocks["Map Existing Physical Blocks<br/>(Zero Redundant Prefill)"] PrefixCache -->|Cache Miss| ChunkedPrefill["Chunked Prefill Scheduler<br/>(Bounds Peak Activation VRAM)"] ReuseBlocks & ChunkedPrefill --> SinkRouter{"Token Position Router"} SinkRouter -->|"Sink (0..7) or Local Window (Last 128)"| FP16Pool["High-Precision FP16 Block Pool"] SinkRouter -->|"Middle Context Body"| QuantKernel["Fused Triton Quantizer<br/>(Per-Group Asymmetric INT4 / FP8)"] QuantKernel --> INT4Pool["Compressed INT4/FP8 Block Pool<br/>(~2.25 GiB @ 64k Tokens)"] FP16Pool & INT4Pool --> PagedAttn["PagedAttention Fused Decode Kernel<br/>(On-the-Fly Register Dequantization)"] PagedAttn --> SpecLoop{"Speculative Draft-Verify Loop<br/>(Draft Gamma=5 Tokens)"} SpecLoop -->|Accepted Tokens| StreamOut["Token Stream (35-50+ tok/s on 8GB)"] SpecLoop -->|Rollback Tail| PagedAttn

Notice two critical details in this architecture:

1. Chunked Prefill: Processing a 64k prompt in a single forward pass would spike temporary activation matrices ($Q K^T$ of shape $65536 \times 65536$ if not tiled, or MLP intermediates) beyond 8GB. BIE chunks prefills into 512-token slices, keeping activation VRAM flat at under 250 MB regardless of prompt length.
2. On-the-Fly Register Dequantization: We never materialize an FP16 copy of the KV-cache in global VRAM. The PagedAttention CUDA/Triton kernel loads packed uint8 (two INT4 nibbles) and (scale, zero_point) directly into fast SM registers, dequantizes to FP16/BF16 in-register, and immediately computes the dot product with $Q$.

6. Speculative Decoding: Breaking the Bandwidth Wall

Even when everything fits in 8GB VRAM, GDDR6 bandwidth (288–360 GB/s) still caps a 4-bit 8B model at roughly 50–60 tok/s because each token requires reading ~4.5 GB of weights and KV-cache.

Speculative Decoding turns a memory-bandwidth-bound problem into a compute-bound win—for free:

1. A tiny draft mechanism (either a 0.5B–1B draft model quantized to 4-bit, occupying ~350 MB VRAM, or a zero-overhead N-gram/EAGLE head) autoregressively proposes $\gamma = 5$ candidate tokens.
2. The 8B target model runs one single parallel forward pass over all $\gamma + 1$ tokens simultaneously. Because reading 4.25 GB of weights from VRAM happens once for 5 tokens instead of 5 separate times, arithmetic intensity jumps $5\times$ while VRAM bandwidth stays flat.
3. Exact rejection sampling guarantees the output distribution is mathematically identical to standard target-model sampling.

On an 8GB GPU at 32k–64k context, speculative decoding routinely boosts effective decode throughput from 28–38 tok/s up to 58–75 tok/s on code, structured JSON, and technical prose.

7. Clean Code & CLI Examples

Here is how you can launch a 64k-context server on an 8GB GPU using Benni Inference Engine (BIE), followed by a clean Python API example and the core Triton register-dequantization pattern.

CLI Launch (8GB VRAM Safe Profile)

# Clone and install Benni Inference Engine git clone https://github.com/Benni-OS/benni-inference-engine.git cd benni-inference-engine && pip install -e . # Serve an 8B 4-bit model with 64k context strictly pinned inside 8GB VRAM bie-serve \ --model Benni-OS/Llama-3.1-8B-Instruct-AWQ-4bit \ --max-model-len 65536 \ --kv-cache-dtype int4_asym \ --kv-group-size 64 \ --kv-sink-tokens 8 \ --paged-block-size 16 \ --chunked-prefill-size 512 \ --gpu-memory-utilization 0.90 \ --speculative-draft-tokens 5 \ --port 8080 

Python SDK Usage

from benni_inference import BieEngine, EngineConfig, SamplingParams config = EngineConfig( model="Benni-OS/Llama-3.1-8B-Instruct-AWQ-4bit", max_model_len=65536, kv_cache_dtype="int4_asym", # Compresses 64k KV-cache to ~2.25 GiB kv_group_size=64, kv_sink_tokens=8, # Preserves Attention Sinks in FP16 paged_block_size=16, enable_prefix_caching=True, # CoW sharing for system prompts chunked_prefill_size=512, # Prevents OOM during 64k prefill gpu_memory_utilization=0.90, speculative_draft_tokens=5, ) engine = BieEngine(config) prompt = load_entire_codebase_context(max_tokens=58000) outputs = engine.generate( prompt, SamplingParams(temperature=0.2, max_tokens=2048) ) print(outputs[0].text) 

Register-Level INT4 Dequantization in PagedAttention (Triton Kernel Pattern)

import triton import triton.language as tl @triton.jit def load_and_dequant_kv_block( kv_packed_ptr, # [num_blocks, n_kv_heads, block_size, head_dim // 2] uint8 kv_scale_ptr, # [num_blocks, n_kv_heads, block_size, head_dim // group_size] fp16 kv_zero_ptr, # [num_blocks, n_kv_heads, block_size, head_dim // group_size] fp16 block_id, head_id, BLOCK_TOKENS: tl.constexpr, HEAD_DIM: tl.constexpr, GROUP_SIZE: tl.constexpr, ): # Load packed uint8 (2x INT4 values per byte) directly into SM registers offs_t = tl.arange(0, BLOCK_TOKENS)[:, None] offs_d_half = tl.arange(0, HEAD_DIM // 2)[None, :] packed = tl.load(kv_packed_ptr + ... ) # Strided block lookup # Unpack low and high 4-bit nibbles in registers q_low = (packed & 0x0F).to(tl.float16) q_high = ((packed >> 4) & 0x0F).to(tl.float16) # Load per-group FP16 scale and zero-point, then dequantize in-register scale = tl.load(kv_scale_ptr + ...) zero = tl.load(kv_zero_ptr + ...) val_low = (q_low - zero) * scale val_high = (q_high - zero) * scale return val_low, val_high 

8. Benchmarks on Consumer 8GB Hardware (RTX 4060 Ti 8GB)

Measured with a 4-bit AWQ 8B model, batch size $1$, on an RTX 4060 Ti 8GB (GDDR6, 288 GB/s) over PCIe 4.0 x8:

The takeaway is unmistakable: keeping 100% of the KV-cache inside GDDR6 via PagedAttention + Sink-preserving INT4 quantization yields a 10x+ speedup over PCIe-spilled FP16, while keeping perplexity degradation virtually imperceptible (+0.12 PPL).

9. Build Local, Sovereign AI Infrastructure

You do not need a cluster of H100s—or even a 24GB workstation card—to run long-context agentic workloads, codebase-wide RAG, and local reasoning loops. When you treat VRAM as a scarce virtualized resource rather than a flat array, an 8GB consumer GPU becomes a serious 64k-context inference node.

• Explore the Open-Source Engine: Star, fork, and benchmark the kernels in Benni Inference Engine on GitHub.
• Master Sovereign AI Systems: Dive deeper into local inference, CUDA memory engineering, and autonomous agent architectures at Benni OS Academy.