diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 97ad947fa..19d865b6e 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -5,14 +5,14 @@ body:
   - type: markdown
     attributes:
       value: |
-        **Support scope for 0.1.0** (see the support matrix in the README):
+        **Support scope** (see the support matrix in the README):
 
-        - **Validated:** 4 × V100 32 GB PCIe, TP4 + DCP2, Qwen3.8 Flash-Next
+        - **Validated:** 4 × V100 32 GB, TP4 + DCP2, Qwen3.8 Flash-Next
           (multimodal) with the AWQ g32, standard NVFP4 or NVIDIA FP4-Local
           checkpoints; E4M3 or INT8 KV cache; KV offload off, CPU, or CPU + SSD;
-          PCIe switch with P2P. PCIe without P2P was validated by disabling P2P on
-          P2P-capable hardware.
-        - **Unverified, best effort:** NVLink, TP2/TP8, V100 16 GB, other GPU counts,
+          NVLink, or a PCIe switch with P2P (the recommended interconnects). PCIe
+          without P2P was validated by disabling P2P on P2P-capable hardware.
+        - **Unverified, best effort:** TP2/TP8, V100 16 GB, other GPU counts,
           other models and other topologies.
         - **Out of scope:** GPUs other than SM70, ROCm/CPU/XPU/TPU, GGUF.
           INT8 KV with the SSD tier is not supported in 0.1.0.
@@ -52,7 +52,7 @@ body:
       options:
         - PCIe switch with P2P
         - PCIe without P2P
-        - NVLink (unverified)
+        - NVLink
         - Other (describe in the reproduction)
     validations:
       required: true
diff --git a/.github/SUPPORT.md b/.github/SUPPORT.md
index 5b1e38d67..dd8d75509 100644
--- a/.github/SUPPORT.md
+++ b/.github/SUPPORT.md
@@ -5,17 +5,17 @@ In short:
 
 | Area | Scope |
 | --- | --- |
-| GPUs | 4 × NVIDIA V100 32 GB PCIe |
-| PCIe switch with P2P | Validated |
+| GPUs | 4 × NVIDIA V100 32 GB (PCIe or SXM2) |
+| PCIe switch with P2P | Recommended; validated |
 | PCIe without P2P | Validated by disabling P2P on P2P-capable hardware; about 30–55% of P2P speed |
-| NVLink | Community results on 4 × V100-SXM2 32 GB ([#2](https://github.com/Leonccaa/ReVolta/issues/2)); outside our release gate |
+| NVLink | Recommended; tested with community results on 4 × V100-SXM2 32 GB ([#2](https://github.com/Leonccaa/ReVolta/issues/2), [#38](https://github.com/Leonccaa/ReVolta/issues/38), [#37](https://github.com/Leonccaa/ReVolta/issues/37)) |
 | Host memory | 128 GiB recommended; 80 GiB measured minimum with KV offload off |
 | KV offload | Off, CPU, or CPU + SSD tier |
 | KV cache | E4M3 (default) or INT8; INT8 with the SSD tier is not supported |
 | Models | Qwen3.8 Flash-Next (QSA, multimodal) |
 | Checkpoints | AWQ g32, standard NVFP4 `RadixArk/Qwen3.8-Flash-Next-NVFP4`, `nvidia/Qwen3.8-Flash-Next-FP4-Local` |
 
-Configurations outside our release gate (NVLink, TP2/TP8, V100 16 GB, other models or
+Configurations outside our release gate (TP2/TP8, V100 16 GB, other models or
 topologies) are handled on a best-effort basis. Other GPU architectures,
 ROCm/CPU/XPU/TPU serving and GGUF are not built.
 
diff --git a/README.md b/README.md
index 69c60dbbc..32fd8fb16 100644
--- a/README.md
+++ b/README.md
@@ -4,24 +4,24 @@
 
 ReVolta turns a server with V100 32 GB GPUs and plenty of system memory into a dependable daily AI service. It runs Qwen3.8 Flash-Next for text, images and video behind an OpenAI-compatible API. ReVolta is built on [1Cat-vLLM](https://github.com/1CatAI/1Cat-vLLM), a V100-focused fork of vLLM, so the `vllm` command, its options and its OpenAI-compatible API work as usual.
 
-It is built for four or more V100 32 GB GPUs and validated on four. Every release passes the same gate on our own server before it ships: single-request greedy output identical to the previous release token for token, 4 and 8 concurrent requests, images and video, CPU cache readback, and 512K-token prompts. That server runs ReVolta every day.
+It is built for four or more V100 32 GB GPUs and validated on four. We recommend, and test, the two interconnects that give the best speed: NVLink, or all GPUs under one PCIe switch with P2P. Every release passes the same gate on our own server before it ships: single-request greedy output identical to the previous release token for token, 4 and 8 concurrent requests, images and video, CPU cache readback, and 512K-token prompts. That server runs ReVolta every day.
 
-**You need:** 4 × V100 32 GB (PCIe with P2P, or NVLink), at least 64 GB of system memory (128 GB recommended), Linux x86-64 with glibc 2.34 or newer, and an NVIDIA driver for CUDA 12.8. Tested hardware and limits: [Support and known limits](docs/known-limits.md).
+**You need:** 4 × V100 32 GB (NVLink or PCIe; NVLink or one PCIe switch with P2P recommended), at least 64 GB of system memory (128 GB recommended), Linux x86-64 with glibc 2.34 or newer, and an NVIDIA driver for CUDA 12.8. Tested hardware and limits: [Support and known limits](docs/known-limits.md).
 
-Current release: [0.1.3](https://github.com/Leonccaa/ReVolta/releases/tag/v0.1.3).
+Current release: [0.1.4](https://github.com/Leonccaa/ReVolta/releases/tag/v0.1.4).
 
 ## At a glance
 
-**Context and capacity** (Uncensored AWQ, E4M3 KV cache, 0.1.3)
+**Context and capacity** (Uncensored AWQ, E4M3 KV cache, 0.1.4)
 
 | | MTP4 (default) | DFlash2 (recommended settings) |
 | --- | ---: | ---: |
 | Context per request | 512K tokens | 512K tokens |
-| GPU KV pool | 1.15 million tokens | **1.38 million tokens** |
-| GPU KV pool / 512K tokens (capacity ratio) | 2.20× | **2.63×** |
+| GPU KV pool | 1.15 million tokens | **1.37 million tokens** |
+| GPU KV pool / 512K tokens (capacity ratio) | 2.20× | **2.61×** |
 | Concurrent requests | 8 | 8 |
 
-Prefixes evicted from the GPU stay in CPU memory or on disk: a cached 32K-token document is answered in **1.28 s instead of 10.06 s**. See [KV offload](docs/kv-offload.md).
+Prefixes evicted from the GPU stay in CPU memory or on disk: a cached 32K-token document comes back from CPU memory in **0.6 s**, and after an engine restart from a local NVMe disk tier in **1.5 s**, instead of **10.1 s** when recomputed (0.1.4, DFlash2). See [KV offload](docs/kv-offload.md).
 
 **Speed** (total output tokens/s, 512-token responses; best of MTP4 and DFlash2)
 
@@ -36,7 +36,7 @@ Versions, power caps and the per-method breakdown are in [Performance](docs/perf
 
 | Model | Weights (bits per weight: routed experts / whole model) | Download | PLE table in host memory | GPU KV pool (MTP4) | Quality vs its BF16 parent: KL ↓ / top-1 ↑ | Notes |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| [Uncensored AWQ g32](https://huggingface.co/leoncca/Qwen3.8-Flash-Next-Uncensored-AWQ-g32) **(recommended)** | 4-bit AWQ g32 experts, FP8 PLE, BF16 rest (**4.63 / 5.95**) | 142 GB, MTP draft included | 48 GiB | 1.15 M (1.38 M with DFlash2) | 0.0214 / 97.1% | Uncensored fine-tune; the reference setup |
+| [Uncensored AWQ g32](https://huggingface.co/leoncca/Qwen3.8-Flash-Next-Uncensored-AWQ-g32) **(recommended)** | 4-bit AWQ g32 experts, FP8 PLE, BF16 rest (**4.63 / 5.95**) | 142 GB, MTP draft included | 48 GiB | 1.15 M (1.37 M with DFlash2) | 0.0214 / 97.1% | Uncensored fine-tune; the reference setup |
 | [Base AWQ g32](https://huggingface.co/leoncca/Qwen3.8-Flash-Next-AWQ-g32) | 4-bit AWQ g32 experts, FP8 PLE, BF16 rest (**4.63 / 5.95**) | 142 GB | 48 GiB | Same layout as Uncensored | 0.0207 / 97.1% | The original model |
 | [Standard NVFP4](https://huggingface.co/RadixArk/Qwen3.8-Flash-Next-NVFP4) | NVFP4 experts, FP8 PLE, BF16 rest (**4.50 / 5.86**) | 135 GB + 5 GB draft pack | 48 GiB | 1.25 M | 0.0303 / 96.4% | Widely used community format |
 | [NVIDIA FP4-Local](https://huggingface.co/nvidia/Qwen3.8-Flash-Next-FP4-Local) | NVFP4 experts and PLE, MXFP8 attention and shared experts (**4.26 / 4.53**) | 102 GB, MTP layer built in | **27 GiB** | **1.47 M** | 0.0380 / 95.9% | NVIDIA's official checkpoint; least host memory |
@@ -53,14 +53,14 @@ The package is `revolta`; the Python import and CLI remain `vllm`.
 ```bash
 python3.12 -m venv /data/revolta/venv
 . /data/revolta/venv/bin/activate
-pip install revolta==0.1.3
-vllm --version          # 0.1.3
+pip install revolta==0.1.4
+vllm --version          # 0.1.4
 ```
 
 Or pull the Docker image:
 
 ```bash
-docker pull ghcr.io/leonccaa/revolta:0.1.3
+docker pull ghcr.io/leonccaa/revolta:0.1.4
 ```
 
 Follow [Getting started](docs/getting-started.md) to download the pinned checkpoint,
@@ -83,10 +83,10 @@ For another checkpoint, use its [preparation guide](docs/checkpoints.md) before
 
 ## Known limits
 
-- V100 16 GB, TP2/TP8, and other models have not been tested; NVLink has community results and is outside the release gate.
+- V100 16 GB, TP2/TP8, and other models have not been tested.
 - This release builds SM70 only; other GPU architectures, ROCm, CPU, XPU, TPU, and GGUF are outside the build.
 - Greedy outputs at C2–C8 can change between runs; fresh compiler autotuning can also change outputs.
-- The AWQ PLE table needs about 48 GiB of host memory by default. An existing experimental option keeps the whole table on disk instead; functional checks in 0.1.3 ran under 64 GiB and 32 GiB container limits without CPU KV offload, but speed was not measured. See [Support and known limits](docs/known-limits.md).
+- The AWQ PLE table needs about 48 GiB of host memory by default. An experimental option keeps the whole table on a local SSD instead: in 0.1.4 it served under 64 GiB and 32 GiB memory limits, also with a 32 GiB CPU KV pool under 64 GiB, and greedy output matched the default path. Single-request decoding is about 23–25% slower, and a cold first token takes 1.7–2.2 times as long. See [Support and known limits](docs/known-limits.md).
 - PLE cascade (spilling part of the table to disk, `ple_disk_cascade=true`) is not supported yet and is off by default in the ReVolta profile; full support is planned for 0.2.
 - INT8 KV cannot be combined with DFlash2; the disk tier does not support INT8 yet.
 
@@ -98,8 +98,10 @@ ReVolta focuses on dependable daily service on V100. Releases check output corre
 long-context retrieval, concurrent use, multimodal input, cache recovery, and speed
 on the reference machine.
 
-Next: faster INT8 KV decoding, simpler NVFP4 setup, INT8 disk caching, and community
-testing on NVLink systems. Longer-term work includes reducing host-memory use and
+Next (0.1.5): faster all-reduce on NVLink above 16 tokens per step, a cheaper DFlash2
+state replay, smaller DFlash2 CPU and disk cache pages, and a 512K long-context
+retrieval self-test. Also planned: faster INT8 KV decoding, simpler NVFP4 setup and
+INT8 disk caching. Longer-term work includes reducing host-memory use and
 runtime dependencies.
 
 ## Getting help
@@ -113,7 +115,10 @@ Reports from NVLink V100 owners are especially welcome; share measured results w
 [Test results](https://github.com/Leonccaa/ReVolta/issues/new?template=test_results.md) template
 ([`.github/ISSUE_TEMPLATE/test_results.md`](.github/ISSUE_TEMPLATE/test_results.md); agents can fill it in and
 open the issue through the API). Thanks to
-[@jttdev](https://github.com/jttdev) for sharing their results ([#2](https://github.com/Leonccaa/ReVolta/issues/2)). Report security issues
+[@jttdev](https://github.com/jttdev), [@That80-20](https://github.com/That80-20) and
+[@vincentyan2023](https://github.com/vincentyan2023) for sharing their results
+([#2](https://github.com/Leonccaa/ReVolta/issues/2), [#38](https://github.com/Leonccaa/ReVolta/issues/38),
+[#37](https://github.com/Leonccaa/ReVolta/issues/37)). Report security issues
 through GitHub private vulnerability reporting.
 
 For project status and development background, read the [pinned overview (#1)](https://github.com/Leonccaa/ReVolta/issues/1).
diff --git a/docs/known-limits.md b/docs/known-limits.md
index 65562fe50..7f7ae82c2 100644
--- a/docs/known-limits.md
+++ b/docs/known-limits.md
@@ -3,6 +3,9 @@
 This reference lists the tested support scope and known limitations. Check it before
 using the [launch tutorial](getting-started.md) or changing hardware, formats or request limits.
 
+ReVolta is designed for four or more V100 32 GB GPUs and validated on four. NVLink, or
+all GPUs under one PCIe switch with P2P, are the recommended and tested interconnects.
+
 The reference system uses four V100 32 GB PCIe cards behind one PCIe switch
 (PIX/PXB in `nvidia-smi topo -m`), with P2P enabled and a 128 GiB container memory limit.
 
@@ -10,7 +13,7 @@ The reference system uses four V100 32 GB PCIe cards behind one PCIe switch
 | --- | --- |
 | 4 × V100 32 GB PCIe, TP4 + DCP2, P2P | Full acceptance passed on the reference configuration |
 | P2P disabled on the same machine | Correct output; about 30–55% of P2P throughput |
-| 4 × V100-SXM2 32 GB, NVLink (NV2 mesh) | Community test by [@jttdev](https://github.com/jttdev) ([#2](https://github.com/Leonccaa/ReVolta/issues/2)): speed, retrieval to about 490K tokens, vision. Not part of our release gate |
+| 4 × V100-SXM2 32 GB, NVLink | Tested by the community: [@jttdev](https://github.com/jttdev) ([#2](https://github.com/Leonccaa/ReVolta/issues/2)): speed, retrieval to about 490K tokens, vision; also [#38](https://github.com/Leonccaa/ReVolta/issues/38) and [#37](https://github.com/Leonccaa/ReVolta/issues/37) |
 | V100 16 GB, other GPU counts | Not yet tested |
 
 **Full acceptance on the reference setup:** default Uncensored AWQ, Standard NVFP4,
@@ -18,8 +21,7 @@ and FP4-Local with their documented settings. Base AWQ has quality measurements.
 80 GiB RAM has memory-stress results. Disk caching has readback results; its full
 release gate passed with an 8 GiB CPU pool and a 40 GiB disk budget.
 
-V100 16 GB, TP2/TP8, and other models have not been tested. NVLink has a community
-test ([#2](https://github.com/Leonccaa/ReVolta/issues/2)) but is not part of our release gate.
+V100 16 GB, TP2/TP8, and other models have not been tested.
 This release builds SM70 only; other GPU architectures, ROCm, CPU, XPU, TPU, and
 GGUF are outside the build.
 
@@ -44,14 +46,14 @@ GGUF are outside the build.
   and is off by default in the ReVolta profile; full support is planned for 0.2.
   Profiling with it enabled used about 4 GiB more GPU memory per 32 GB card, which
   left room for about 345K context tokens.
-- **Whole PLE table on disk (experimental; first checked in 0.1.3).** Instead of pinned host memory, the
+- **Whole PLE table on disk (experimental; first checked in 0.1.3, measured in 0.1.4).** Instead of pinned host memory, the
   whole table can stay as file-backed mappings of the checkpoint's safetensors files,
   read by a separate PLE process. This is a different path from cascade and is off by
   default. To try it, set `VLLM_PLE_CPU_OFFLOAD=1`, `VLLM_PLE_DISK_OFFLOAD=1` and
   `VLLM_SM70_QWEN38_HYBRID_PLE=0`, add `--safetensors-load-strategy lazy`, keep
-  `"ple_disk_cascade": false` in `--kernel-config` (the check also set
-  `"ple_disk_release_pages": true`), and run without CPU KV offload
-  (`--kv-transfer-config`); that combination was not tested.
+  `"ple_disk_cascade": false` in `--kernel-config` (the checks also set
+  `"ple_disk_release_pages": true`). CPU KV offload (`--kv-transfer-config`) can stay
+  on; 0.1.4 measured it with a 32 GiB CPU pool.
     - Checked on the 0.1.3 candidate with Uncensored AWQ and E4M3 KV: MTP4 served
       131K-token prompts, 4 concurrent requests and images under both a 64 GiB and a
       32 GiB container memory limit, and a 491K-token prompt under 64 GiB; DFlash2
@@ -62,9 +64,21 @@ GGUF are outside the build.
       single-request outputs diverged at a near-tie, so the two paths are not bitwise
       identical. If the PLE process dies, the client gets an error after about 9 s and
       the engine exits after 11–13 s instead of hanging.
-    - Not measured: decode speed and time to first token, a machine with little
-      physical RAM, a local SSD (the check read the checkpoint from network storage),
-      and the net memory saving against the default path.
+    - Measured on 0.1.4 with DFlash2 (recommended settings), Uncensored AWQ, E4M3 KV and
+      250 W, with the table read from a local NVMe or SATA SSD. Under a 64 GiB limit
+      (NVMe and SATA), a 32 GiB limit (NVMe), and a 64 GiB limit with a 32 GiB CPU KV
+      pool (NVMe and SATA), the engine started and served without running out of memory,
+      and three 64-token greedy outputs matched the pinned-memory path exactly.
+      Single-request decoding was 101.5–104.9 tokens/s against 135.5 with the pinned
+      table, about 23–25% slower. A cold first token took 5.4–5.9 / 16.8–17.9 /
+      65.6–69.8 s at 8K / 32K / 131K prompt tokens, against 2.61 / 9.66 / 40.40 s, so
+      1.7–2.2 times as long; once the table pages for a prompt had been read, the first
+      token was within 2–3% of the pinned path (5–8% with the 32 GiB CPU KV pool). NVMe
+      and SATA, and the 32 and 64 GiB limits, differed little. Peak memory of the
+      serving container was about 57 GiB under the 64 GiB limit (62 GiB with the CPU KV
+      pool) and 30 GiB under 32 GiB.
+    - Not measured: a machine with little physical RAM (the limits above were set on the
+      container that runs the engine), and long-running stability on this path.
 - **FP4-Local vision.** Keep the default vision-tower TP4. Forcing data parallelism
   removes the tested memory arrangement. Non-empty request-level
   `mm_processor_kwargs` and `media_io_kwargs` are rejected with HTTP 400 by
diff --git a/docs/kv-offload.md b/docs/kv-offload.md
index d7607ae82..3186b6333 100644
--- a/docs/kv-offload.md
+++ b/docs/kv-offload.md
@@ -30,8 +30,11 @@ computed again.
 }
 ```
 
-`cpu_bytes_to_use` sets the pool size (total for all workers). With 48 GiB, the pool
-holds about 2.4M tokens. `mamba_state_slots_reference_tokens` sets how many
+`cpu_bytes_to_use` sets the pool size (total for all workers). With MTP4, a 48 GiB pool
+holds about 2.4M tokens. With DFlash2, the drafter uses a different cache block size, so
+every cache group takes pages of the full block size and a prefix needs about 50 KB per
+token in the CPU pool and on disk (measured in 0.1.4: 1.84 GB for a 32K-token prefix,
+6.36 GB for 131K), about 2.5 times as much as with MTP4. `mamba_state_slots_reference_tokens` sets how many
 recurrent-state slots each layer group gets. A smaller reference gives more slots,
 which helps when many short sessions share the pool.
 
@@ -78,15 +81,21 @@ use a larger CPU pool and set `max_size_bytes` to your disk budget.
 - The SSD tier is a disposable cache, not model storage.
 - A prefix restored from disk is staged in the CPU pool first, so `cpu_bytes_to_use` must
   be larger than the largest prefix you expect to restore from disk (about 21 KB per
-  token; a 100K-token prefix needs more than 2 GiB). Otherwise the request is computed
+  token with MTP4 and about 50 KB with DFlash2; a 100K-token prefix needs more than
+  2 GiB with MTP4 and about 5 GB with DFlash2). Otherwise the request is computed
   again. The quick-start 48 GiB pool holds about 2.4M tokens.
 - The SSD tier is **not supported with INT8 KV** yet. That combination is
   rejected at configuration time.
 
-Validation scope: the SSD tier was tested with its root on a network file system. The
-full release gate passed with an 8 GiB CPU pool and a 40 GiB disk budget, including a
-99K-token prefix restored from disk (96,288 cached tokens, output identical). SSD
-throughput was not measured.
+Validation scope: the full release gate passed with the SSD tier on a network file
+system (8 GiB CPU pool, 40 GiB disk budget), including a 99K-token prefix restored from
+disk (96,288 cached tokens, output identical). In 0.1.4 the tier was also measured on a
+local NVMe SSD with DFlash2 (7 GiB CPU pool, 64 GiB disk budget, one read and one write
+thread). After an engine restart, 11 of 11 documents were read back from disk with the
+same 64-token greedy output as a cold run, at about 2.2–2.4 GB/s per request; a
+32K / 99K / 131K-token prefix came back in 1.47 / 2.93 / 3.77 s instead of
+10.12 / 31.88 / 42.43 s when recomputed. A root holding 27.8 GB in 1,088 files did not
+slow the restart. See [Coming back to a document](performance.md#coming-back-to-a-document).
 
 ## See also
 
diff --git a/docs/performance.md b/docs/performance.md
index 89aaf2328..225814448 100644
--- a/docs/performance.md
+++ b/docs/performance.md
@@ -26,7 +26,26 @@ method (MTP4 and DFlash2) and prompt type is in [Performance](#performance).
 
 ### Coming back to a document
 
-No ReVolta version is specified for this readback measurement; its request and cache conditions follow below.
+Measured on 0.1.4 with DFlash2 (recommended settings), Uncensored AWQ, E4M3 KV, 250 W,
+the pinned PLE table, a 7 GiB CPU pool and a disk tier on a local NVMe SSD. Each
+document was first computed cold. The CPU readback followed after the GPU prefix cache
+was cleared; the disk readback followed an engine restart with the page cache dropped.
+Both readbacks hit the whole cacheable prefix, and the disk readbacks gave the same
+64-token greedy output as the cold run.
+
+| Prompt tokens | Recomputed | From CPU memory | From NVMe after a restart |
+| ---: | ---: | ---: | ---: |
+| 32,768 | 10.12 s | 0.61 s | 1.47 s |
+| 99,000 | 31.88 s | 0.77 s | 2.93 s |
+| 131,072 | 42.43 s | 0.99 s | 3.77 s |
+
+Times are to the first token, one request at a time. Eight more 32K-token documents
+gave 0.62–0.66 s from CPU memory and 1.41–1.46 s from NVMe for the first four of them.
+Four 32K-token documents read from NVMe at the same time after the restart took
+1.48–3.84 s each (median 2.67 s).
+
+An earlier measurement, with no ReVolta version recorded, under the request and cache
+conditions below:
 
 A cached 32K-token document answered a follow-up in **1.28 seconds from memory**,
 compared with **10.06 seconds when recomputed**. Both produced identical output.
@@ -39,8 +58,8 @@ compared with **10.06 seconds when recomputed**. Both produced identical output.
 | CPU memory + filesystem cache | 1.60 s |
 
 The request generated 64 tokens and reused 31,008 cached prompt tokens. A small GPU
-pool forced eviction. The filesystem tier was tested on a network filesystem;
-local SSD performance has not yet been measured.
+pool forced eviction. The filesystem tier in this earlier measurement was on a network
+filesystem.
 
 ### Quantization quality